A mathematician’s guide to plasmids: an introduction to plasmid biology for modellers

Dewan, Ian; Uecker, Hildegard

doi:10.1099/mic.0.001362

Cited by 9 publications

(10 citation statements)

References 287 publications

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“…The link between genotypic and phenotypic resistance can become increasingly complicated when bacteria acquire plasmids that confer resistance. A more comprehensive understanding of the variables driving the spread of antibiotic resistance among bacterial populations can be obtained by examining plasmid-mediated resistance mechanisms ( Dewan and Uecker, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

Application of next-generation sequencing to identify different pathogens

Nafea,

Wang,

Wang

et al. 2024

Front. Microbiol.

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Early and precise detection and identification of various pathogens are essential for epidemiological monitoring, disease management, and reducing the prevalence of clinical infectious diseases. Traditional pathogen detection techniques, which include mass spectrometry, biochemical tests, molecular testing, and culture-based methods, are limited in application and are time-consuming. Next generation sequencing (NGS) has emerged as an essential technology for identifying pathogens. NGS is a cutting-edge sequencing method with high throughput that can create massive volumes of sequences with a broad application prospects in the field of pathogen identification and diagnosis. In this review, we introduce NGS technology in detail, summarizes the application of NGS in that identification of different pathogens, including bacteria, fungi, and viruses, and analyze the challenges and outlook for using NGS to identify clinical pathogens. Thus, this work provides a theoretical basis for NGS studies and provides evidence to support the application of NGS in distinguishing various clinical pathogens.

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Section: Introductionmentioning

confidence: 99%

Application of next-generation sequencing to identify different pathogens

Nafea,

Wang,

Wang

et al. 2024

Front. Microbiol.

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“…Horizontal gene transfer has been suggested as another mechanism to stabilize cooperation in bacteria [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. Horizontal transfer of genes for cooperation could increase relatedness at those loci and prevent invasion by non-cooperative cheats.…”

Section: Introductionmentioning

confidence: 99%

Genes for cooperation are not more likely to be carried by plasmids

Dewar,

Belcher,

Scott

et al. 2024

Proc. R. Soc. B.

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Cooperation is prevalent across bacteria, but risks being exploited by non-cooperative cheats. Horizontal gene transfer, particularly via plasmids, has been suggested as a mechanism to stabilize cooperation. A key prediction of this hypothesis is that genes which are more likely to be transferred, such as those on plasmids, should be more likely to code for cooperative traits. Testing this prediction requires identifying all genes for cooperation in bacterial genomes. However, previous studies used a method which likely misses some of these genes for cooperation. To solve this, we used a new genomics tool, SOCfinder, which uses three distinct modules to identify all kinds of genes for cooperation. We compared where these genes were located across 4648 genomes from 146 bacterial species. In contrast to the prediction of the hypothesis, we found no evidence that plasmid genes are more likely to code for cooperative traits. Instead, we found the opposite—that genes for cooperation were more likely to be carried on chromosomes. Overall, the vast majority of genes for cooperation are not located on plasmids, suggesting that the more general mechanism of kin selection is sufficient to explain the prevalence of cooperation across bacteria.

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“…However, these density estimates are usually plotted with a logarithmic scale on the x -axis (plasmid length): when we plotted plasmid length data (pooled across bacterial families) on a linear scale (Dewan and Uecker 2023), the bimodal distribution disappeared (compare panels A and C of Figure 1). Choosing a log scale, which is a natural thing to do given that the lengths of plasmids extend over four orders of magnitude, has distorted the estimated distribution of plasmid lengths.…”

Section: Introductionmentioning

confidence: 99%

“…Only plasmids of length less than 300 000 bp are shown for reasons of scale: 1844 plasmids in the database (5.34 % of the total) are longer than the limit; the longest plasmid in the database is 4 605 385 bp. Based on Figure 1 of Dewan and Uecker (2023). Panel B shows the same distribution as panel A, but with plasmid length on a log scale.…”

Section: Introductionmentioning

confidence: 99%

“…These graphs show densities with a logarithmic scale on the x -axis, which is a natural choice given that the lengths of plasmids extend over four orders of magnitude: however, when we plotted plasmid length data (pooled across bacterial families) on a linear scale (Dewan and Uecker 2023), the bimodal distribution disappeared (see also diCenzo and Finan 2017 who also use a linear scale and find a unimodal distribution). In this letter, we explain the reason for this discrepancy, and what implications, if any, it has for our understanding of plasmids.…”

Section: Introductionmentioning

confidence: 99%

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Is the distribution of plasmid lengths bimodal?

Dewan,

Uecker

2023

Preprint

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The length of a plasmid is a key property which contributes to many aspects of plasmid biology. When distributions of plasmid lengths are shown in the literature, they are usually plotted with length on a logarithmic scale. However, a quantity and its logarithm have distinct distributions which may differ considerably in shape. Mistaking the distribution of log-lengths for the distribution of lengths can therefore lead to distorted conclusions about the distribution; in particular, the distribution of log-lengths may be bimodal when the distribution of lengths is only unimodal. This particular confusion has arisen in the literature where a bimodal log-length distribution is suggested to support a distinction between large and small classes of plasmids. While the length distribution is indeed bimodal within many bacterial families, it is not across the ensemble of all plasmids. We suggest that authors should be careful to show the plasmid length distribution, or to distinguish the two distributions, to avoid misleading inferences.HighlightsThe distributions of lengths and log-lengths of plasmids are different, and have considerably different shapes.The typical practice of using a logarithmic scale for plasmid lengths leads to confusion between the two distributions.In particular, the distribution of log-lengths can be bimodal when the distribution of lengths is not.The length distribution within bacterial families is often bimodal, but across all plasmids, it is unimodal.Clearly distinguishing between the distributions will ensure that biological conclusions drawn from them are robust.

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A mathematician’s guide to plasmids: an introduction to plasmid biology for modellers

Cited by 9 publications

References 287 publications

Application of next-generation sequencing to identify different pathogens

Application of next-generation sequencing to identify different pathogens

Genes for cooperation are not more likely to be carried by plasmids

Is the distribution of plasmid lengths bimodal?

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