Modelling microbiome recovery after antibiotics using a stability landscape framework

Shaw, Liam P.; Bassam, Hassan; Barnes, C.; Walker, A. Sarah; Klein, Nigel; Balloux, François

doi:10.1101/222398

Cited by 35 publications

(46 citation statements)

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“…An alternative explanation, however, is that the microbiome is meta-stable, and antibiotic perturbation may transition the structure to an alternative, yet still stable (meta-stable) composition with similar numbers of species and diversity. Indeed, modeling existing datasets of healthy humans treated with antibiotics supports this assertion [56,74,100].…”

Section: Stable Adulthoodmentioning

confidence: 85%

“…1) [73]. Indeed, computational modeling of the short-term impact of various antibiotics demonstrates that ciprofloxacin, a broad-spectrum fluoroquinolone antibiotic, showed more displacement from microbiome equilibrium than amoxicillin, a narrow spectrum β-lactam antibiotic [74]. Certain strains of the same species do not recover, suggesting potentially lasting consequences.…”

Section: Stable Adulthoodmentioning

confidence: 99%

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Understanding the impact of antibiotic perturbation on the human microbiome

2020

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The human gut microbiome is a dynamic collection of bacteria, archaea, fungi, and viruses that performs essential functions for immune development, pathogen colonization resistance, and food metabolism. Perturbation of the gut microbiome’s ecological balance, commonly by antibiotics, can cause and exacerbate diseases. To predict and successfully rescue such perturbations, first, we must understand the underlying taxonomic and functional dynamics of the microbiome as it changes throughout infancy, childhood, and adulthood. We offer an overview of the healthy gut bacterial architecture over these life stages and comment on vulnerability to short and long courses of antibiotics. Second, the resilience of the microbiome after antibiotic perturbation depends on key characteristics, such as the nature, timing, duration, and spectrum of a course of antibiotics, as well as microbiome modulatory factors such as age, travel, underlying illness, antibiotic resistance pattern, and diet. In this review, we discuss acute and chronic antibiotic perturbations to the microbiome and resistome in the context of microbiome stability and dynamics. We specifically discuss key taxonomic and resistance gene changes that accompany antibiotic treatment of neonates, children, and adults. Restoration of a healthy gut microbial ecosystem after routine antibiotics will require rationally managed exposure to specific antibiotics and microbes. To that end, we review the use of fecal microbiota transplantation and probiotics to direct recolonization of the gut ecosystem. We conclude with our perspectives on how best to assess, predict, and aid recovery of the microbiome after antibiotic perturbation.

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Section: Stable Adulthoodmentioning

confidence: 85%

Section: Stable Adulthoodmentioning

confidence: 99%

Understanding the impact of antibiotic perturbation on the human microbiome

2020

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“…For example, a single standard course of antibiotics has been shown to alter species composition of the gut and oral microbiomes for over a year. 69,70 Matching of cases and controls is particularly challenging for metagenomics studies given the broad range of microbes considered. 71 Metagenomics studies should aim to minimise and statistically control for host confounders or, at a minimum, list those confounders that might affect interpretation of results.…”

Section: Figure 1: Sources Of Uncertainty Diagram Highlighting Potentmentioning

confidence: 99%

STROBE-metagenomics: a STROBE extension statement to guide the reporting of metagenomics studies

Bharucha

Oeser

Balloux

et al. 2020

The Lancet Infectious Diseases

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The term metagenomics refers to the use of sequencing methods to simultaneously identify genomic material from all organisms present in a sample, with the advantage of greater taxonomic resolution than culture or other methods. Applications include pathogen detection and discovery, species characterisation, antimicrobial resistance detection, virulence profiling, and study of the microbiome and microecological factors affecting health. However, metagenomics involves complex and multistep processes and there are important technical and methodological challenges that require careful consideration to support valid inference. We coordinated a multidisciplinary, international expert group to establish reporting guidelines that address specimen processing, nucleic acid extraction, sequencing platforms, bioinformatics considerations, quality assurance, limits of detection, power and sample size, confirmatory testing, causality criteria, cost, and ethical issues. The guidance recognises that metagenomics research requires pragmatism and caution in interpretation, and that this field is rapidly evolving.

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“…Shaw et al . (Shaw et al 2019) reported a simple quantitative model based on a stability landscape framework. They demonstrated that the microbiome existed in multiple stable equilibria of landscape that could be influenced by sufficiently strong perturbations such as antibiotics that could alter the microbiome from its normal equilibrium to other states.…”

Section: Discussionmentioning

confidence: 99%

Antibiotic-dependent relationships between nasal microbiome and secreted proteome in chronic rhinosinusitis and nasal polyps

Kim

et al. 2020

Preprint

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BackgroundAntibiotics are commonly prescribed to treat chronic rhinosinusitis (CRS); however, the effects of antibiotics on the microbiome and secreted proteome remain unknown in regard to CRS.ObjectiveWe analyzed the effects of antibiotics on the nasal microbiome and secreted proteome in the context of CRS using multi-omic analysis.MethodsNasal secretions were collected from 29 control, 30 CRS patients without nasal polyps (CRSsNP), and 40 CRS patients with nasal polyps (CRSwNP). A total of 99 subjects were divided into two groups that included subjects who had taken antibiotics 3 months prior to sampling (ABX) and those who had not (NABX). We performed 16S rDNA sequence analyses and Orbitrap mass spectrometry-based proteomic analyses in data-independent acquisition (DIA) on the nasal secretions. Spearman correlation was used to assess the correlations between the nasal microbiome and secreted proteome.ResultsWe observed a strong association between the nasal microbiome and secreted proteome according to disease status. Antibiotic use reduced differences in the microbial community and secreted proteome according to disease status. Interestingly, in nasal polyp (NP) patients, antibiotics exhibited strong effects not only on the nasal microbiome and the secreted proteome but also on their associations. Additionally, their correlations were strengthened in subjects who had taken antibiotics.ConclusionIntegrative analyses revealed that the correlations between the microbiome and the secreted proteome could be altered and strengthened in subjects who used antibiotics. These findings provide novel insight into the effects of antibiotics on the nasal environment and the host responses in CRS.

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Modelling microbiome recovery after antibiotics using a stability landscape framework

Cited by 35 publications

References 50 publications

Understanding the impact of antibiotic perturbation on the human microbiome

Understanding the impact of antibiotic perturbation on the human microbiome

STROBE-metagenomics: a STROBE extension statement to guide the reporting of metagenomics studies

Antibiotic-dependent relationships between nasal microbiome and secreted proteome in chronic rhinosinusitis and nasal polyps

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