Maternal gut and breast milk microbiota affect infant gut antibiotic resistome and mobile genetic elements

Nature Communications, Journal Year: 2018, Volume and Issue: 9(1)

Published: Sept. 18, 2018

Abstract The infant gut microbiota has a high abundance of antibiotic resistance genes (ARGs) compared to adults, even in the absence exposure. Here we study potential sources ARGs by performing metagenomic sequencing breast milk, as well and maternal microbiomes. We find that fecal ARG mobile genetic element (MGE) profiles infants are more similar those their own mothers than unrelated mothers. MGEs mothers’ milk also shared with infants. Termination breastfeeding intrapartum prophylaxis mothers, which have affect microbial community composition, associated higher abundances specific ARGs, composition is largely shaped bacterial phylogeny gut. Our results suggest inherit legacy past consumption via transmission genes, but still strongly impacts overall load.

Language: Английский

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Innovative and rapid antimicrobial susceptibility testing systems

Nature Reviews Microbiology, Journal Year: 2020, Volume and Issue: 18(5), P. 299 - 311

Published: Feb. 13, 2020

Language: Английский

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Advancing functional and translational microbiome research using meta-omics approaches

Microbiome, Journal Year: 2019, Volume and Issue: 7(1)

Published: Dec. 1, 2019

The gut microbiome has emerged as an important factor affecting human health and disease. recent development of –omics approaches, including phylogenetic marker-based profiling, shotgun metagenomics, metatranscriptomics, metaproteomics, metabolomics, enabled efficient characterization microbial communities. These techniques can provide strain-level taxonomic resolution the taxa present in microbiomes, assess potential functions encoded by community quantify metabolic activities occurring within a complex microbiome. application these meta-omics approaches to clinical samples identified species, pathways, metabolites that are associated with treatment diseases. findings have further facilitated microbiome-targeted drug discovery efforts improve management. Recent vitro vivo investigations uncovered presence extensive drug-microbiome interactions. interactions also been shown be contributors disparate patient responses often observed during disease therapy. Therefore, developing or frameworks enable rapid screening, detailed evaluation, accurate prediction drug/host-microbiome is critically modern era research precision medicine. Here we review current status techniques, integrative multi-omics for characterizing microbiome's functionality context We summarize discuss new applying assays study Lastly, exemplify strategies implementing microbiome-based medicines using high throughput assays.

Language: Английский

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Persistent metagenomic signatures of early-life hospitalization and antibiotic treatment in the infant gut microbiota and resistome

Nature Microbiology, Journal Year: 2019, Volume and Issue: 4(12), P. 2285 - 2297

Published: Sept. 9, 2019

Language: Английский

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Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

Frontiers in Microbiology, Journal Year: 2021, Volume and Issue: 12

Published: Feb. 19, 2021

The number of microbiome-related studies has notably increased the availability data on human microbiome composition and function. These provide essential material to deeply explore host-microbiome associations their relation development progression various complex diseases. Improved data-analytical tools are needed exploit all information from these biological datasets, taking into account peculiarities data, i.e., compositional, heterogeneous sparse nature datasets. possibility predicting host-phenotypes based taxonomy-informed feature selection establish an association between predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights models that can be used outputs, such as classification prediction in microbiology, infer host phenotypes diseases use microbial communities stratify patients by characterization state-specific signatures. Here we review state-of-the-art ML methods respective software applied studies, performed part COST Action ML4Microbiome activities. This scoping focuses application related clinical diagnostics, prognostics, therapeutics. Although presented here more bacterial community, many algorithms could general, regardless type. literature covering broad topic aligned with methodology. manual identification sources been complemented with: (1) automated publication search through digital libraries three major publishers using natural language processing (NLP) Toolkit, (2) relevant repositories GitHub ranking research papers relying rank approach.

Language: Английский

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Prediction of antibiotic resistance in Escherichia coli from large-scale pan-genome data

PLoS Computational Biology, Journal Year: 2018, Volume and Issue: 14(12), P. e1006258 - e1006258

Published: Dec. 14, 2018

The emergence of microbial antibiotic resistance is a global health threat. In clinical settings, the key to controlling spread resistant strains accurate and rapid detection. As traditional culture-based methods are time consuming, genetic approaches have recently been developed for this task. detection typically made by measuring few known determinants previously identified from genome sequencing, thus requires prior knowledge its biological mechanisms. To overcome limitation, we employed machine learning models predict 11 compounds across four classes antibiotics existing novel whole sequences 1936 E. coli strains. We considered range methods, examined population structure, isolation year, gene content, polymorphism information as predictors. Gradient boosted decision trees consistently outperformed alternative with an average accuracy 0.91 on held-out data (range 0.81-0.97). While best most frequently score 0.79 could be obtained using structure alone. Single nucleotide variation were less useful, significantly improved prediction only two antibiotics, including ciprofloxacin. These results demonstrate that in can accurately predicted without priori mechanisms, both genomic epidemiological informative. This paves way integrating into diagnostic tools clinic.

Language: Английский

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Antibiotic perturbations to the gut microbiome

Nature Reviews Microbiology, Journal Year: 2023, Volume and Issue: 21(12), P. 772 - 788

Published: July 25, 2023

Language: Английский

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A Review and Tutorial of Machine Learning Methods for Microbiome Host Trait Prediction

Frontiers in Genetics, Journal Year: 2019, Volume and Issue: 10

Published: June 25, 2019

With the growing importance of microbiome research, there is increasing evidence that host variation in microbial communities associated with overall health. Advancement genetic sequencing methods for microbiomes has coincided improvements machine learning, important implications disease risk prediction humans. One aspect specific to use taxonomy-informed feature selection. In this review non-experts, we explore most commonly used learning methods, and evaluate their accuracy as applied trait prediction. Methods are described at an introductory level, R/Python code analyses provided.

Language: Английский

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Accelerating antibiotic discovery through artificial intelligence

Communications Biology, Journal Year: 2021, Volume and Issue: 4(1)

Published: Sept. 9, 2021

By targeting invasive organisms, antibiotics insert themselves into the ancient struggle of host-pathogen evolutionary arms race. As pathogens evolve tactics for evading antibiotics, therapies decline in efficacy and must be replaced, distinguishing from most other forms drug development. Together with a slow expensive antibiotic development pipeline, proliferation drug-resistant drives urgent interest computational methods that promise to expedite candidate discovery. Strides artificial intelligence (AI) have encouraged its application multiple dimensions computer-aided design, increasing This review describes AI-facilitated advances discovery both small molecule antimicrobial peptides. Beyond essential prediction activity, emphasis is also given compound representation, determination drug-likeness traits, resistance, de novo molecular design. Given urgency resistance crisis, we analyze uptake open science best practices AI-driven argue openness reproducibility as means accelerating preclinical research. Finally, trends literature areas future inquiry are discussed, artificially intelligent enhancements at large offer many opportunities applications

Language: Английский

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Necrotizing enterocolitis is preceded by increased gut bacterial replication, Klebsiella , and fimbriae-encoding bacteria

Science Advances, Journal Year: 2019, Volume and Issue: 5(12)

Published: Dec. 6, 2019

Metagenomic analysis identifies microbial signatures preceding necrotizing enterocolitis development in premature infants.

Language: Английский

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