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Estimating the transfer rates of bacterial plasmids with an adapted Luria–Delbrück fluctuation analysis

To increase our basic understanding of the ecology and evolution of conjugative plasmids, we need reliable estimates of their rate of transfer between bacterial cells. Current assays to measure transfer rate are based on deterministic modeling frameworks. However, some cell numbers in these assays c...

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Autores principales: Kosterlitz, Olivia, Muñiz Tirado, Adamaris, Wate, Claire, Elg, Clint, Bozic, Ivana, Top, Eva M., Kerr, Benjamin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9352209/
https://www.ncbi.nlm.nih.gov/pubmed/35877684
http://dx.doi.org/10.1371/journal.pbio.3001732
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author Kosterlitz, Olivia
Muñiz Tirado, Adamaris
Wate, Claire
Elg, Clint
Bozic, Ivana
Top, Eva M.
Kerr, Benjamin
author_facet Kosterlitz, Olivia
Muñiz Tirado, Adamaris
Wate, Claire
Elg, Clint
Bozic, Ivana
Top, Eva M.
Kerr, Benjamin
author_sort Kosterlitz, Olivia
collection PubMed
description To increase our basic understanding of the ecology and evolution of conjugative plasmids, we need reliable estimates of their rate of transfer between bacterial cells. Current assays to measure transfer rate are based on deterministic modeling frameworks. However, some cell numbers in these assays can be very small, making estimates that rely on these numbers prone to noise. Here, we take a different approach to estimate plasmid transfer rate, which explicitly embraces this noise. Inspired by the classic fluctuation analysis of Luria and Delbrück, our method is grounded in a stochastic modeling framework. In addition to capturing the random nature of plasmid conjugation, our new methodology, the Luria–Delbrück method (“LDM”), can be used on a diverse set of bacterial systems, including cases for which current approaches are inaccurate. A notable example involves plasmid transfer between different strains or species where the rate that one type of cell donates the plasmid is not equal to the rate at which the other cell type donates. Asymmetry in these rates has the potential to bias or constrain current transfer estimates, thereby limiting our capabilities for estimating transfer in microbial communities. In contrast, the LDM overcomes obstacles of traditional methods by avoiding restrictive assumptions about growth and transfer rates for each population within the assay. Using stochastic simulations and experiments, we show that the LDM has high accuracy and precision for estimation of transfer rates compared to the most widely used methods, which can produce estimates that differ from the LDM estimate by orders of magnitude.
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spelling pubmed-93522092022-08-05 Estimating the transfer rates of bacterial plasmids with an adapted Luria–Delbrück fluctuation analysis Kosterlitz, Olivia Muñiz Tirado, Adamaris Wate, Claire Elg, Clint Bozic, Ivana Top, Eva M. Kerr, Benjamin PLoS Biol Methods and Resources To increase our basic understanding of the ecology and evolution of conjugative plasmids, we need reliable estimates of their rate of transfer between bacterial cells. Current assays to measure transfer rate are based on deterministic modeling frameworks. However, some cell numbers in these assays can be very small, making estimates that rely on these numbers prone to noise. Here, we take a different approach to estimate plasmid transfer rate, which explicitly embraces this noise. Inspired by the classic fluctuation analysis of Luria and Delbrück, our method is grounded in a stochastic modeling framework. In addition to capturing the random nature of plasmid conjugation, our new methodology, the Luria–Delbrück method (“LDM”), can be used on a diverse set of bacterial systems, including cases for which current approaches are inaccurate. A notable example involves plasmid transfer between different strains or species where the rate that one type of cell donates the plasmid is not equal to the rate at which the other cell type donates. Asymmetry in these rates has the potential to bias or constrain current transfer estimates, thereby limiting our capabilities for estimating transfer in microbial communities. In contrast, the LDM overcomes obstacles of traditional methods by avoiding restrictive assumptions about growth and transfer rates for each population within the assay. Using stochastic simulations and experiments, we show that the LDM has high accuracy and precision for estimation of transfer rates compared to the most widely used methods, which can produce estimates that differ from the LDM estimate by orders of magnitude. Public Library of Science 2022-07-25 /pmc/articles/PMC9352209/ /pubmed/35877684 http://dx.doi.org/10.1371/journal.pbio.3001732 Text en © 2022 Kosterlitz et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Methods and Resources
Kosterlitz, Olivia
Muñiz Tirado, Adamaris
Wate, Claire
Elg, Clint
Bozic, Ivana
Top, Eva M.
Kerr, Benjamin
Estimating the transfer rates of bacterial plasmids with an adapted Luria–Delbrück fluctuation analysis
title Estimating the transfer rates of bacterial plasmids with an adapted Luria–Delbrück fluctuation analysis
title_full Estimating the transfer rates of bacterial plasmids with an adapted Luria–Delbrück fluctuation analysis
title_fullStr Estimating the transfer rates of bacterial plasmids with an adapted Luria–Delbrück fluctuation analysis
title_full_unstemmed Estimating the transfer rates of bacterial plasmids with an adapted Luria–Delbrück fluctuation analysis
title_short Estimating the transfer rates of bacterial plasmids with an adapted Luria–Delbrück fluctuation analysis
title_sort estimating the transfer rates of bacterial plasmids with an adapted luria–delbrück fluctuation analysis
topic Methods and Resources
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9352209/
https://www.ncbi.nlm.nih.gov/pubmed/35877684
http://dx.doi.org/10.1371/journal.pbio.3001732
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