Applying ecological resistance and resilience to dissect bacterial antibiotic responses

An essential property of microbial communities is the ability to survive a disturbance. Survival can be achieved through resistance, the ability to absorb effects of a disturbance without a notable change, or resilience, the ability to recover after being perturbed by a disturbance. These concepts have long been applied to the analysis of ecological systems, although their interpretations are often subject to debate. Here, we show that this framework readily lends itself to the dissection of the bacterial response to antibiotic treatment, where both terms can be unambiguously defined. The ability to tolerate the antibiotic treatment in the short term corresponds to resistance, which primarily depends on traits associated with individual cells. In contrast, the ability to recover after being perturbed by an antibiotic corresponds to resilience, which primarily depends on traits associated with the population. This framework effectively reveals the phenotypic signatures of bacterial pathogens expressing extended-spectrum β-lactamases (ESBLs) when treated by a β-lactam antibiotic. Our analysis has implications for optimizing treatment of these pathogens using a combination of a β-lactam and a β-lactamase (Bla) inhibitor. In particular, our results underscore the need to dynamically optimize combination treatments based on the quantitative features of the bacterial response to the antibiotic or the Bla inhibitor.

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Dissecting bacterial resistance and resilience in antibiotic responses

10.1101/305482 ◽

2018 ◽

Author(s):

Hannah R. Meredith ◽

Virgile Andreani ◽

Allison J. Lopatkin ◽

Anna J. Lee ◽

Deverick J. Anderson ◽

...

Keyword(s):

Microbial Communities ◽

Antibiotic Treatment ◽

Bacterial Resistance ◽

Bacterial Pathogens ◽

Ecological Systems ◽

Essential Property ◽

Short Term ◽

Bacterial Response ◽

Combination Treatments ◽

Lactam Antibiotic

AbstractAn essential property of microbial communities is the ability to survive a disturbance. Survival can be achieved throughresistance, the ability to absorb effects of a disturbance without a significant change, orresilience, the ability to recover after being perturbed by a disturbance. These concepts have long been applied to the analysis of ecological systems, though their interpretations are often subject to debate. Here we show that this framework readily lends itself to the dissection of the bacterial response to antibiotic treatment, where both terms can be unambiguously defined. The ability to tolerate the antibiotic treatment in the short term corresponds to resistance, which primarily depends on traits associated with individual cells. In contrast, the ability to recover after being perturbed by an antibiotic corresponds to resilience, which primarily depends on traits associated with the population. This framework effectively reveals the phenotypic signatures of bacterial pathogens expressing extended spectrum β-lactamases (ESBLs), when treated by a β-lactam antibiotic. Our analysis has implications for optimizing treatment of these pathogens using a combination of a β-lactam and a β-lactamase (Bla) inhibitor. In particular, our results underscore the need to dynamically optimize combination treatments based on the quantitative features of the bacterial response to the antibiotic or the Bla inhibitor.

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Non-responder phenotype reveals apparent microbiome-wide antibiotic tolerance in the murine gut

Communications Biology ◽

10.1038/s42003-021-01841-8 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Christian Diener ◽

Anna C. H. Hoge ◽

Sean M. Kearney ◽

Ulrike Kusebauch ◽

Sushmita Patwardhan ◽

...

Keyword(s):

Antibiotic Treatment ◽

Treatment Duration ◽

Efflux Transporters ◽

High Dose ◽

Specific Host ◽

Lactam Antibiotic ◽

Enteric Infections ◽

Community Tolerance ◽

Future Work ◽

Antibiotic Treatment Duration

AbstractBroad spectrum antibiotics cause both transient and lasting damage to the ecology of the gut microbiome. Antibiotic-induced loss of gut bacterial diversity has been linked to susceptibility to enteric infections. Prior work on subtherapeutic antibiotic treatment in humans and non-human animals has suggested that entire gut communities may exhibit tolerance phenotypes. In this study, we validate the existence of these community tolerance phenotypes in the murine gut and explore how antibiotic treatment duration or a diet enriched in antimicrobial phytochemicals might influence the frequency of this phenotype. Almost a third of mice exhibited whole-community tolerance to a high dose of the β-lactam antibiotic cefoperazone, independent of antibiotic treatment duration or dietary phytochemical amendment. We observed few compositional differences between non-responder microbiota during antibiotic treatment and the untreated control microbiota. However, gene expression was vastly different between non-responder microbiota and controls during treatment, with non-responder communities showing an upregulation of antimicrobial tolerance genes, like efflux transporters, and a down-regulation of central metabolism. Future work should focus on what specific host- or microbiome-associated factors are responsible for tipping communities between responder and non-responder phenotypes so that we might learn to harness this phenomenon to protect our microbiota from routine antibiotic treatment.

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Antibiotic treatment with one single dose of gentamicin at admittance in addition to a β-lactam antibiotic in the treatment of community-acquired bloodstream infection with sepsis

PLoS ONE ◽

10.1371/journal.pone.0236864 ◽

2020 ◽

Vol 15 (7) ◽

pp. e0236864 ◽

Cited By ~ 1

Author(s):

Karolina Liljedahl Prytz ◽

Mårten Prag ◽

Hans Fredlund ◽

Anders Magnuson ◽

Martin Sundqvist ◽

...

Keyword(s):

Single Dose ◽

Bloodstream Infection ◽

Antibiotic Treatment ◽

Lactam Antibiotic

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Contributions of Lactobacillus plantarum PC170 administration on the recovery of gut microbiota after short-term ceftriaxone exposure in mice

Beneficial Microbes ◽

10.3920/bm2019.0191 ◽

2020 ◽

Vol 11 (5) ◽

pp. 489-509

Author(s):

R. Cheng ◽

H. Liang ◽

Y. Zhang ◽

J. Guo ◽

Z. Miao ◽

...

Keyword(s):

Body Weight ◽

Gut Microbiota ◽

Lactobacillus Plantarum ◽

Antibiotic Treatment ◽

Recovery Phase ◽

The Body ◽

Faecal Microbiota ◽

Short Term ◽

Host Animal ◽

The Impact

This study aimed to determine the impact of Lactobacillus plantarum PC170 concurrent with antibiotic treatment and/or during the recovery phase after antibiotic treatment on the body weight, faecal bacterial composition, short-chain fatty acids (SCFAs) concentration, and splenic cytokine mRNA expression of mice. Orally administrated ceftriaxone quantitatively and significantly decreased body weight, faecal total bacteria, Akkermansia muciniphila, and Lactobacillus plantarum, and faecal SCFAs concentration. Ceftriaxone treatment also dramatically altered the faecal microbiota with an increased Chao1 index, decreased species diversities and Bacteroidetes, and more Firmicutes and Proteobacteria. After ceftriaxone intervention, these changes all gradually started to recover. However, faecal microbiota diversities were still totally different from control by significantly increased α- and β-diversities. Bacteroidetes all flourished and became dominant during the recovery process. However, mice treated with PC170 both in parallel with and after ceftriaxone treatment encouraged more Bacteroidetes, Verrucomicrobia, and Actinobacteria, and the diversity by which to make faecal microbiota was very much closer to control. Furthermore, the expression of splenic pro-inflammatory cytokine tumour necrosis factor-α mRNA in mice supplemented with PC170 during the recovery phase was significantly lower than natural recovery. These results indicated that antibiotics, such as ceftriaxone, even with short-term intervention, could dramatically damage the structure of gut microbiota and their abilities to produce SCFAs with loss of body weight. Although such damages could be partly recovered with the cessation of antibiotics, the implication of antibiotics to gut microbiota might remain even after antibiotic treatment. The selected strain PC170 might be a potential probiotic because of its contributions in helping the host animal to remodel or stabilise its gut microbiome and enhancing the anti-inflammatory response as protection from the side effects of antibiotic therapy when it was administered in parallel with and after antibiotic treatment.

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Mass balanced trophic models and short-term dynamical simulations for benthic ecological systems of Mejillones and Antofagasta bays (SE Pacific): Comparative network structure and assessment of human impacts

Ecological Modelling ◽

10.1016/j.ecolmodel.2015.04.006 ◽

2015 ◽

Vol 309-310 ◽

pp. 153-162 ◽

Cited By ~ 20

Author(s):

Marco Ortiz ◽

Fernando Berrios ◽

Leonardo Campos ◽

Roberto Uribe ◽

Alejandro Ramirez ◽

...

Keyword(s):

Network Structure ◽

Human Impacts ◽

Ecological Systems ◽

Short Term ◽

Dynamical Simulations ◽

Trophic Models

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A trajectories' guide to the state space - learning missing terms in bifurcating ecological systems

10.5194/egusphere-egu21-16159 ◽

2021 ◽

Author(s):

Rahel Vortmeyer-Kley ◽

Pascal Nieters ◽

Gordon Pipa

Keyword(s):

Machine Learning ◽

Differential Equations ◽

Algal Blooms ◽

Ecological Systems ◽

Short Term ◽

Specific System ◽

Worst Case ◽

System Components ◽

Full Knowledge

Ecological systems typically can exhibit various states ranging from extinction to coexistence of different species in oscillatory states. The switch from one state to another is called bifurcation. All these behaviours of a specific system are hidden in a set of describing differential equations (DE) depending on different parametrisations. To model such a system as DE requires full knowledge of all possible interactions of the system components. In practise, modellers can end up with terms in the DE that do not fully describe the interactions or in the worst case with missing terms.The framework of universal differential equations (UDE) for scientific machine learning (SciML) [1] allows to reconstruct the incomplete or missing term from an idea of the DE and a short term timeseries of the system and make long term predictions of the system&#8217;s behaviour. However, the approach in [1] has difficulties to reconstruct the incomplete or missing term in systems with bifurcations. We developed a trajectory-based loss metric for UDE and SciML to tackle the problem and tested it successfully on a system mimicking algal blooms in the ocean.[1] Rackauckas, Christopher, et al. "Universal differential equations for scientific machine learning." arXiv preprint arXiv:2001.04385 (2020).

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Outlier Detection for Minor Compositional Variations in Taxonomic Abundance Data

Applied Sciences ◽

10.3390/app9071355 ◽

2019 ◽

Vol 9 (7) ◽

pp. 1355

Author(s):

Koji Ishiya ◽

Sachiyo Aburatani

Keyword(s):

Microbial Communities ◽

Natural Environments ◽

Human Gut ◽

Short Term ◽

Computational Framework ◽

Dietary Changes ◽

Compositional Changes ◽

Compositional Variations ◽

Living Organisms ◽

Shed Light

To understand the activities of complex microbial communities in various natural environments and living organisms, we need to capture the compositional changes in their taxonomic abundance. Here, we propose a new computational framework to detect compositional changes in microorganisms, including minor bacteria. This framework is designed to statistically assess relative variations in taxonomic abundance. By using this approach, we detected compositional changes in the human gut microbiome that might be associated with short-term human dietary changes. Our approach can shed light on the compositional changes of minor microorganisms that are easily overlooked.

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