Metagenomic species profiling using universal phylogenetic marker genes

Sunagawa, Shinichi; Mende, Daniel R.; Zeller, Georg; Izquierdo-Carrasco, Fernando; Berger, Simon; Kultima, Jens Roat; Coelho, Luís Pedro; Arumugam, Manimozhiyan; Tap, Julien; Nielsen, Henrik Bjørn; Rasmussen, Simon; Brunak, Søren; Pedersen, Oluf; Guarner, Francisco; Vos, Willem M. de; Wang, Jun; Li, Junhua; Doré, Joël; Ehrlich, S. Dusko; Stamatakis, Alexandros; Bork, Peer

doi:10.1038/nmeth.2693

Cited by 462 publications

(439 citation statements)

References 34 publications

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“…2). Similar to earlier analyses 38 , 43 ± 14% of the detected microorganisms in each sample belonged to mOTUs without a sequenced isolate genome. The relative abundance of single mOTUs not assigned to any phylum reached up to 9% (Supplementary Section 2).…”

Section: Study Cohort and Microbial Community Structuressupporting

confidence: 85%

“…mOTU analysis for taxonomic profiling was separately performed for trimmed and filtered metagenomic and metatranscriptomic reads for each sample using the MOCAT pipeline 38,55 and the mOTU.v1.padded reference database ( Supplementary Fig. 21), which comprises genes for the ribosome-binding ATPase (YchF; GTP1/OBG family), phenylalanyl-tRNA-synthetase alpha subunit, arginyltRNA-, seryl-tRNA-, cysteinyl-tRNA-, leucyl-tRNA-and valyl-tRNA-synthetase, tRNA A37 threonylcarbamoyltransferase (TsaD), and two signal recognition particle GTPases (COG0012, COG0016, COG0018, COG0172, COG0215, COG0495, COG0525, COG0533, COG0541 and COG0552, respectively) 38 . Metagenomic and metatranscriptomic codes to recognize donors were built from mOTU relative abundances and evaluated using Idability 39 with the -meta_mode setting.…”

Section: Methodsmentioning

confidence: 99%

“…38) marker genes and of metagenomic and metatranscriptomic reads mapping to functionally annotated genes were used for differential analyses by applying the R package DESeq2 (ref. 94) (see Supplementary Table 1 for numbers of samples per individual).…”

Section: Methodsmentioning

confidence: 99%

“…To obtain overviews of the microbial community structures in 53 well-preserved faecal samples collected over a period of two to four months, we calculated relative abundances of metagenomic operational taxonomic units (mOTUs) 38 Table 2). In most cases, mOTU-based taxonomic profiles were stable over time within individuals.…”

Section: Study Cohort and Microbial Community Structuresmentioning

confidence: 99%

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Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes

et al. 2016

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The gastrointestinal microbiome is a complex ecosystem with functions that shape human health. Studying the relationship between taxonomic alterations and functional repercussions linked to disease remains challenging. Here, we present an integrative approach to resolve the taxonomic and functional attributes of gastrointestinal microbiota at the metagenomic, metatranscriptomic and metaproteomic levels. We apply our methods to samples from four families with multiple cases of type 1 diabetes mellitus (T1DM). Analysis of intra-and inter-individual variation demonstrates that family membership has a pronounced effect on the structural and functional composition of the gastrointestinal microbiome. In the context of T1DM, consistent taxonomic differences were absent across families, but certain human exocrine pancreatic proteins were found at lower levels. The associated microbial functional signatures were linked to metabolic traits in distinct taxa. The methodologies and results provide a foundation for future large-scale integrated multi-omic analyses of the gastrointestinal microbiome in the context of host-microbe interactions in human health and disease.

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Section: Study Cohort and Microbial Community Structuressupporting

confidence: 85%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Study Cohort and Microbial Community Structuresmentioning

confidence: 99%

See 2 more Smart Citations

Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes

et al. 2016

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“…Throughout the manuscript, we used specI clusters at the species level related via the NCBI taxonomy database as a taxonomic reference. Additionally, mOTU abundances were also determined using standard MOCAT procedures (Sunagawa et al , 2013), but exclusively used to estimate species diversity (Table EV1). …”

Section: Methodsmentioning

confidence: 99%

Subspecies in the global human gut microbiome

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Coelho

Sunagawa

et al. 2017

Molecular Systems Biology

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Population genomics of prokaryotes has been studied in depth in only a small number of primarily pathogenic bacteria, as genome sequences of isolates of diverse origin are lacking for most species. Here, we conducted a large‐scale survey of population structure in prevalent human gut microbial species, sampled from their natural environment, with a culture‐independent metagenomic approach. We examined the variation landscape of 71 species in 2,144 human fecal metagenomes and found that in 44 of these, accounting for 72% of the total assigned microbial abundance, single‐nucleotide variation clearly indicates the existence of sub‐populations (here termed subspecies). A single subspecies (per species) usually dominates within each host, as expected from ecological theory. At the global scale, geographic distributions of subspecies differ between phyla, with Firmicutes subspecies being significantly more geographically restricted. To investigate the functional significance of the delineated subspecies, we identified genes that consistently distinguish them in a manner that is independent of reference genomes. We further associated these subspecies‐specific genes with properties of the microbial community and the host. For example, two of the three Eubacterium rectale subspecies consistently harbor an accessory pro‐inflammatory flagellum operon that is associated with lower gut community diversity, higher host BMI, and higher blood fasting insulin levels. Using an additional 676 human oral samples, we further demonstrate the existence of niche specialized subspecies in the different parts of the oral cavity. Taken together, we provide evidence for subspecies in the majority of abundant gut prokaryotes, leading to a better functional and ecological understanding of the human gut microbiome in conjunction with its host.

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Fecal microbiota transplantation results in bacterial strain displacement in patients with inflammatory bowel diseases

et al. 2019

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Fecal microbiota transplantation (FMT), which is thought to have the potential to correct dysbiosis of gut microbiota, has been used to treat inflammatory bowel disease (IBD) for almost a decade. Here, we report an interventional prospective cohort study performed to elucidate the extent of and processes underlying microbiota engraftment in IBD patients after FMT treatment. The cohort included two categories of patients: (a) patients with moderate to severe Crohn’s disease (CD) (Harvey–Bradshaw Index ≥ 7, n = 11) and (b) patients with ulcerative colitis (UC) (Montreal classification S2 and S3, n = 4). All patients were treated with a single FMT (via mid‐gut, from healthy donors), and follow‐up visits were performed at baseline, 3 days, 1 week, and 1 month after FMT (missing time points included). At each follow‐up time point, fecal samples and clinical metadata were collected. For comparative analysis, 10 fecal samples from 10 healthy donors were included to represent the diversity level of normal gut microbiota. Additionally, the metagenomic data of 25 fecal samples from five individuals with metabolic syndrome who underwent autologous FMT treatment were downloaded from a previous published paper to represent fluctuations in microbiota induced during FMT. All fecal samples underwent shotgun metagenomic sequencing. We found that 3 days after FMT, 11 out of 15 recipients were in remission (three out of four UC recipients; 8 out of 11 CD recipients). Generally, bacterial colonization was observed to be lower in CD recipients than in UC recipients at both species and strain levels. Furthermore, across species, different strains displayed disease‐specific displacement advantages under two‐disease status. Finally, most post‐FMT species (> 80%) could be properly predicted (area under the curve > 85%) using a random forest classification model, with the gut microbiota composition and clinical parameters of pre‐FMT recipients acting as factors that contribute to prediction accuracy.

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Metagenomic species profiling using universal phylogenetic marker genes

Cited by 462 publications

References 34 publications

Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes

Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes

Subspecies in the global human gut microbiome

Fecal microbiota transplantation results in bacterial strain displacement in patients with inflammatory bowel diseases

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