Dataset Information

Tan2012 - Antibiotic Treatment, Inoculum Effect

ABSTRACT:

Tan2012 - Antibiotic Treatment, Inoculum Effect

The efficacy of many antibiotics decreases with increasing bacterial density, a phenomenon called the ‘inoculum effect’ (IE). This study reveals that, for ribosome-targeting antibiotics, IE is due to bistable inhibition of bacterial growth, which reduces the efficacy of certain treatment frequencies.

This model is described in the article:

The inoculum effect and band-pass bacterial response to periodic antibiotic treatment.

Tan C, Phillip Smith R, Srimani JK, Riccione KA, Prasada S, Kuehn M, You L.

Mol Syst Biol. 2012 Oct 9; 8:617

Abstract:

The inoculum effect (IE) refers to the decreasing efficacy of an antibiotic with increasing bacterial density. It represents a unique strategy of antibiotic tolerance and it can complicate design of effective antibiotic treatment of bacterial infections. To gain insight into this phenomenon, we have analyzed responses of a lab strain of Escherichia coli to antibiotics that target the ribosome. We show that the IE can be explained by bistable inhibition of bacterial growth. A critical requirement for this bistability is sufficiently fast degradation of ribosomes, which can result from antibiotic-induced heat-shock response. Furthermore, antibiotics that elicit the IE can lead to 'band-pass' response of bacterial growth to periodic antibiotic treatment: the treatment efficacy drastically diminishes at intermediate frequencies of treatment. Our proposed mechanism for the IE may be generally applicable to other bacterial species treated with antibiotics targeting the ribosomes.

This model is hosted on BioModels Database and identified by: MODEL1208300000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

ORGANISM(S): Escherichia coli

SUBMITTER: Lucian Smith

PROVIDER: MODEL1208300000 | biostudies-other |

SECONDARY ACCESSION(S): 23047527

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

The inoculum effect and band-pass bacterial response to periodic antibiotic treatment.

Tan Cheemeng C Smith Robert Phillip RP Srimani Jaydeep K JK Riccione Katherine A KA Prasada Sameer S Kuehn Meta M You Lingchong L

Molecular systems biology 20120101

PMID: 23047527

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Project description:Tan2012 - Antibiotic Treatment, Inoculum Effect The efficacy of many antibiotics decreases with increasing bacterial density, a phenomenon called the ‘inoculum effect’ (IE). This study reveals that, for ribosome-targeting antibiotics, IE is due to bistable inhibition of bacterial growth, which reduces the efficacy of certain treatment frequencies. This model is described in the article: The inoculum effect and band-pass bacterial response to periodic antibiotic treatment. Tan C, Phillip Smith R, Srimani JK, Riccione KA, Prasada S, Kuehn M, You L. Mol Syst Biol. 2012 Oct 9; 8:617 Abstract: The inoculum effect (IE) refers to the decreasing efficacy of an antibiotic with increasing bacterial density. It represents a unique strategy of antibiotic tolerance and it can complicate design of effective antibiotic treatment of bacterial infections. To gain insight into this phenomenon, we have analyzed responses of a lab strain of Escherichia coli to antibiotics that target the ribosome. We show that the IE can be explained by bistable inhibition of bacterial growth. A critical requirement for this bistability is sufficiently fast degradation of ribosomes, which can result from antibiotic-induced heat-shock response. Furthermore, antibiotics that elicit the IE can lead to 'band-pass' response of bacterial growth to periodic antibiotic treatment: the treatment efficacy drastically diminishes at intermediate frequencies of treatment. Our proposed mechanism for the IE may be generally applicable to other bacterial species treated with antibiotics targeting the ribosomes. This model is hosted on BioModels Database and identified by: MODEL1208300000 . To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Project description:The importance of the mammalian intestinal microbiota to human health has been intensely studied over the past few years. It is now clear that the interactions between human hosts and their associated microbial communities need to be characterized in molecular detail if we are to truly understand human physiology. Additionally, the study of such host-microbe interactions is likely to provide us with new strategies to manipulate such complex systems to maintain or restore homeostasis in order to prevent or cure pathological states. We describe the use of high-throughput metabolomics to shed light on the interactions between the intestinal microbiota and the host. We show that treatment with the antibiotic streptomycin disrupts intestinal homeostasis and has a profound impact on the intestinal metabolome, affecting the levels of over 87% of all metabolites detected. Many metabolic pathways that are critical for host physiology were affected, including bile acid, eicosanoid and steroid hormone synthesis. Interestingly, many of these pathways are also affected by intestinal pathogens. Dissecting the effect of both beneficial and pathogenic bacteria on some of these pathways will be instrumental in understanding the interplay between the host, the resident microbiota and incoming pathogens and may aid in the design of new therapeutic strategies that target these interactions. Age-matched female C57BL/6 mice were used. Fresh feces were collected and stored at -80 oC. Mice were then treated with 20 mg of streptomycin through oral gavage and fresh feces were collected 24 hours after treatment and stored at -80 oC until used. To extract metabolites from feces, acetonitrile was added to samples (approximately 10-25 μL of acetonitrile per 1 mg of tissue), which were then homogenized. The samples were then cleared by centrifugation and the supernatant was collected and dried at room temperature using a centrifuge equipped with a vacuum pump. All extracts were kept at -80 oC until used. For metabolic profiling, the dried extracts from mouse feces were suspended in a 2:3 mixture of water and acetonitrile (10 μL per 1 mg of tissue), vortexed and cleared by centrifugation. Supernatants were collected and used as described below. Extracts were diluted 1:5 with 60% acetonitrile containing either 0.2% formic acid (for positive ion mode) or 0.5% ammonium hydroxide (for negative ion mode) and spiked with predefined amounts of the "ES tuning mix" solution as the internal standard for mass calibration. The solutions were then infused, using a syringe pump (KDS Scientific, Holliston, USA) at a flow rate of 2.5 µL per minute, into a 12-T Apex-Qe hybrid quadrupole-FT-ICR mass spectrometer (Bruker Daltonics, Billerica, USA) equipped with an Apollo II electrospray ionization source, a quadrupole mass filter and a hexapole collision cell. Data were recorded in positive and negative ion modes with broadband detection and an FT acquisition size of 1024 kilobytes per second, within a range of m/z 150 to 1100. Under these settings, a mass resolution of ca. 100,000 (full width at half maximum, FWHM) at m/z 400 and a mass accuracy within 2 ppm or less for all detected components, following internal mass calibration, were observed. Other experimental parameters were: capillary electrospray voltage of 3600-3750 V, spray shield voltage of 3300-3450 V, source ion accumulation time of 0.1 s and collision cell ion accumulation time of 0.2 s. To increase detection sensitivity, survey scan mass spectra in positive and negative ion modes were acquired from the accumulation of 200 scans per spectrum, and duplicate acquisitions per sample were carried out to ensure data reproducibility. Raw mass spectrometry data were processed as described elsewhere (Han et al. 2008. Metabolomics. 4:128-140). To identify differences in metabolite composition between untreated and treated samples, we first filtered our list of masses for metabolites that were present on one set of samples (untreated or treated) but not the other. Additionally, we averaged the mass intensities of metabolites in each group and calculated the ratios between averaged intensities of metabolites from untreated and treated samples. To assign possible metabolite identities to the masses present in only one of the sample groups or showing at least a 2-fold change in intensities between the sample groups, the monoisotopic neutral masses of interest were queried against MassTrix (http://masstrix.org), a free-access software designed to incorporate masses into metabolic pathways. Masses were searched against the Mus musculus database within a mass error of 3 ppm. Data were analyzed by unpaired t tests with 95% confidence intervals.

Dataset Information

Tan2012 - Antibiotic Treatment, Inoculum Effect

Publications

The inoculum effect and band-pass bacterial response to periodic antibiotic treatment.

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