Animals in a bacterial world, a new imperative for the life sciences

Proceedings of the National Academy of Sciences, Journal Year: 2013, Volume and Issue: 110(9), P. 3229 - 3236

Published: Feb. 7, 2013

In the last two decades, widespread application of genetic and genomic approaches has revealed a bacterial world astonishing in its ubiquity diversity. This review examines how growing knowledge vast range animal–bacterial interactions, whether shared ecosystems or intimate symbioses, is fundamentally altering our understanding animal biology. Specifically, we highlight recent technological intellectual advances that have changed thinking about five questions: bacteria facilitated origin evolution animals; do animals affect each other’s genomes; does normal development depend on partners; homeostasis maintained between their symbionts; can ecological deepen multiple levels interaction. As answers to these fundamental questions emerge, all biologists will be challenged broaden appreciation interactions include investigations relationships among partners as seek better natural world.

Language: Английский

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Population-level analysis of gut microbiome variation

Science, Journal Year: 2016, Volume and Issue: 352(6285), P. 560 - 564

Published: April 28, 2016

“Normal” for the gut microbiota For benefit of future clinical studies, it is critical to establish what constitutes a “normal” microbiome, if exists at all. Through fecal samples and questionnaires, Falony et al. Zhernakova targeted general populations in Belgium Netherlands, respectively. Gut composition correlated with range factors including diet, use medication, red blood cell counts, chromogranin A, stool consistency. The data give some hints possible biomarkers normal communities. Science , this issue pp. 560 565

Language: Английский

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Normalization and microbial differential abundance strategies depend upon data characteristics

Microbiome, Journal Year: 2017, Volume and Issue: 5(1)

Published: March 3, 2017

Data from 16S ribosomal RNA (rRNA) amplicon sequencing present challenges to ecological and statistical interpretation. In particular, library sizes often vary over several ranges of magnitude, the data contains many zeros. Although we are typically interested in comparing relative abundance taxa ecosystem two or more groups, can only measure taxon specimens obtained ecosystems. Because comparison specimen is not equivalent ecosystems, this presents a special challenge. Second, because (as well as ecosystem) sum 1, these compositional data. constrained by simplex (sum 1) unconstrained Euclidean space, standard methods analysis applicable. Here, evaluate how impact performance existing normalization differential analyses. Effects on normalization: Most enable successful clustering samples according biological origin when groups differ substantially their overall microbial composition. Rarefying clearly clusters than other techniques do for ordination metrics based presence absence. Alternate measures potentially vulnerable artifacts due size. testing: We build previous work seven proposed using rarefied raw Our simulation studies suggest that false discovery rates abundance-testing increased rarefying itself, although course results loss sensitivity elimination portion available For with large (~10×) differences average size, lowers rate. DESeq2, without addition constant, smaller datasets (<20 per group) but tends towards higher rate samples, very uneven sizes, and/or effects. drawing inferences regarding ecosystem, composition microbiomes (ANCOM) sensitive (for >20 also critically method tested has good control These findings guide which use characteristics given study.

Language: Английский

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Temporal development of the gut microbiome in early childhood from the TEDDY study

Nature, Journal Year: 2018, Volume and Issue: 562(7728), P. 583 - 588

Published: Oct. 1, 2018

The development of the microbiome from infancy to childhood is dependent on a range factors, with microbial–immune crosstalk during this time thought be involved in pathobiology later life diseases1–9 such as persistent islet autoimmunity and type 1 diabetes10–12. However, our knowledge, no studies have performed extensive characterization early large, multi-centre population. Here we analyse longitudinal stool samples 903 children between 3 46 months age by 16S rRNA gene sequencing (n = 12,005) metagenomic 10,867), part Environmental Determinants Diabetes Young (TEDDY) study. We show that developing gut undergoes three distinct phases progression: developmental phase (months 3–14), transitional 15–30), stable 31–46). Receipt breast milk, either exclusive or partial, was most significant factor associated structure. Breastfeeding higher levels Bifidobacterium species (B. breve B. bifidum), cessation milk resulted faster maturation microbiome, marked phylum Firmicutes. Birth mode also significantly phase, driven Bacteroides (particularly fragilis) infants delivered vaginally. increased diversity maturation, regardless birth mode. factors including geographical location household exposures (such siblings furry pets) represented important covariates. A nested case–control analysis revealed subtle associations microbial taxonomy diabetes. These data determine structural functional assembly provide foundation for targeted mechanistic investigation into consequences long-term health. Metagenomic TEDDY study shows breastfeeding structure, microbiome.

Language: Английский

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The neuroactive potential of the human gut microbiota in quality of life and depression

Nature Microbiology, Journal Year: 2019, Volume and Issue: 4(4), P. 623 - 632

Published: Feb. 4, 2019

Language: Английский

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Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis

Microbiome, Journal Year: 2014, Volume and Issue: 2(1)

Published: May 5, 2014

Experimental designs that take advantage of high-throughput sequencing to generate datasets include RNA (RNA-seq), chromatin immunoprecipitation (ChIP-seq), 16S rRNA gene fragments, metagenomic analysis and selective growth experiments. In each case the underlying data are similar composed counts reads mapped a large number features in sample. Despite this similarity, methods used for these experimental all different, do not translate across Alternative have been developed physical geological sciences treat as compositions. Compositional transform relative abundances with result analyses more robust reproducible.Data from an vitro experiment, RNA-seq experiment Human Microbiome Project abundance dataset were examined by ALDEx2, compositional tool uses Bayesian infer technical statistical error. The ALDEx2 approach is shown be suitable three types data: it correctly identifies both direction differential substantially set differentially expressed genes leading tools taxa distinguish tongue dorsum buccal mucosa dataset. design reduces false positive identifications many few samples.Statistical per feature showed R package simple tool, which can applied RNA-seq, datasets, extension other techniques use approach.

Language: Английский

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Metagenomic and metabolomic analyses reveal distinct stage-specific phenotypes of the gut microbiota in colorectal cancer

Nature Medicine, Journal Year: 2019, Volume and Issue: 25(6), P. 968 - 976

Published: June 1, 2019

Language: Английский

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Metagenomic analysis of faecal microbiome as a tool towards targeted non-invasive biomarkers for colorectal cancer

Gut, Journal Year: 2015, Volume and Issue: 66(1), P. 70 - 78

Published: Sept. 25, 2015

To evaluate the potential for diagnosing colorectal cancer (CRC) from faecal metagenomes.We performed metagenome-wide association studies on samples 74 patients with CRC and 54 controls China, validated results in 16 24 Denmark. We further biomarkers two published cohorts France Austria. Finally, we employed targeted quantitative PCR (qPCR) assays to diagnostic of selected an independent Chinese cohort 47 109 controls.Besides confirming known associations Fusobacterium nucleatum Peptostreptococcus stomatis CRC, found significant several species, including Parvimonas micra Solobacterium moorei. identified 20 microbial gene markers that differentiated control microbiomes, 4 Danish cohort. In French Austrian cohorts, these four genes distinguished metagenomes areas under receiver-operating curve (AUC) 0.72 0.77, respectively. qPCR measurements accurately classified AUC=0.84 OR 23. These were enriched early-stage (I-II) patient highlighting using metagenomic early diagnosis CRC.We present first profiling study microbiomes discover validate ethnically different independently affordable clinically relevant technology. Our thus takes a step towards non-invasive samples.

Language: Английский

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Neonatal gut microbiota associates with childhood multisensitized atopy and T cell differentiation

Nature Medicine, Journal Year: 2016, Volume and Issue: 22(10), P. 1187 - 1191

Published: Sept. 12, 2016

Language: Английский

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Quantitative microbiome profiling links gut community variation to microbial load

Nature, Journal Year: 2017, Volume and Issue: 551(7681), P. 507 - 511

Published: Nov. 1, 2017

Language: Английский

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