Salmonella persisters promote the spread of antibiotic resistance plasmids in the gut

Bakkeren, Erik; Huisman, Jana S.; Fattinger, Stefan A.; Hausmann, Annika; Furter, Markus; Egli, Adrian; Slack, Emma; Sellin, Mikael E.; Bonhoeffer, Sebastian; Regoes, Roland R.; Diard, Médéric; Hardt, Wolf‐Dietrich

doi:10.1038/s41586-019-1521-8

Cited by 171 publications

(216 citation statements)

References 39 publications

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“…dimensionless Plate [12] Exconjugant frequency [12] T R dimensionless Filter [23] Gene transfer frequency [23] T D dimensionless Liquid [16,24]; Filter [17,21,23] (Plasmid) transfer frequency [21]; Gene transfer frequency [23]; Conjugation frequency [16,17], Recombinant yield [24], Plasmid transfer efficiency [14] T N dimensionless Liquid [20] Conjugation frequency based on total bacterial count [20] T +D R dimensionless Liquid microcosms [25] Plasmid prevalence [25] T +D N dimensionless Soil microcosms [26] Frequency of plasmid carriage [26] T R+T dimensionless Liquid [8,25], Mouse gut [8,11] Proportion of transconjugants [11,25], Fraction of transconjugants (in recipient population) [8] log 10 T √ DR dimensionless Liquid [27] (Logarithm of) Conjugation rate [27] T DR ml CFU Filter [28] Transconjugant frequency [28] ψmax…”

Section: Measurementioning

confidence: 99%

“…Liquid [18], Plate [10,13] (Plasmid) Transfer rate [10,18], Plasmid transfer efficiency [15], Conjugation rate per mating pair [11], Conjugation coefficient [22] Table 1: Measures of conjugation proficiency and plasmid prevalence or spread reported in the literature. Here D, R, T stands for the population density of donors, recipients, and transconjugants at the time point of measurement, N is the total population density N = D + R + T , N 0 is the initial total population density, R 0 is the initial population density of recipients, ψ max is the maximum growth rate of the mating culture.…”

Section: Measurementioning

confidence: 99%

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Estimating plasmid conjugation rates: a new computational tool and a critical comparison of methods

Huisman

Benz

Duxbury

et al. 2020

Preprint

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Plasmids are important vectors for the spread of genes among diverse populations of bacteria.However, there is no standard method to determine the rate at which they spread horizontally via conjugation. Here, we compare commonly used methods on simulated data, and show that the conjugation rate estimates often depend strongly on the time of measurement, the initial population densities, or the initial ratio of donor to recipient populations. We derive a new 'end-point' measure to estimate conjugation rates, which extends the Simonsen method to include the effects of differences in population growth and conjugation rates from donors and transconjugants.We further derive analytical expressions for the parameter range in which these approximations remain valid. All tools to estimate conjugation rates are available in an R package and Shiny app.The result is a set of guidelines for easy, accurate, and comparable measurement of conjugation rates and tools to verify these rates.Plasmids are extra-chromosomal, self-replicating genetic elements that can spread between bac-1 teria via conjugation. They spread genes within and between bacterial species and are a primary 2 source of genetic innovation in the prokaryotic realm [1,2]. Genes disseminated by plasmids include 3 virulence factors, heavy metal and antibiotic resistance, metabolic genes, as well as genes involved 4 in cooperation and spite [2,3,4,5]. To understand how these traits shape the ecology and evolution 5 of bacteria [6], it is of fundamental importance to understand how plasmids spread. 6

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Section: Measurementioning

confidence: 99%

Section: Measurementioning

confidence: 99%

Estimating plasmid conjugation rates: a new computational tool and a critical comparison of methods

Huisman

Benz

Duxbury

et al. 2020

Preprint

Self Cite

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“…Although tools are available to identify AR genes within the gut 37 microbiome, and characterize their function 7 , abundance 8,9 and their host-associations 10 , no studies have 38 attempted to monitor the bacterial host associations of AR genes and mobile elements during relatively 39 short periods, such as during these patients' hospitalizations. 40 41 To determine the bacterial hosts of mobile AR genes, we utilized a high-throughput chromatin 42 conformation capture (Hi-C) method aimed at sampling long-range interactions within single bacterial 43 genomes 11,12,13 . Briefly, while cells are still intact, DNA within individual cells is crosslinked by 44 formaldehyde.…”

mentioning

confidence: 99%

“…of Enterobacteriaceae in the samples, which have been proposed as mediators of HGT 40 , the total 181 abundance of AR genes, or the number of Hi-C reads (Extended Data Figure 16). Rather, we found that 182 the only correlate was the number of taxa in a sample: as patients' microbiomes became less diverse, the 183 gene-taxa network density increased ( Figure 3B).…”

mentioning

confidence: 99%

“…Additionally, we observe the emergence of novel AR genes in enteric pathogens, originating either from 216 gut commensals or other enteric pathogens, including Enterobacteriaceae. Enterobacteriaceae species are 217 among the most common causes of infection and sepsis in these patients and Enterobacteriaceae from the 218 gut have been shown previously to harbor excessive numbers of AR genes 41 and serve to promote HGT of 219 AR genes 42 . We see the exchange of AR gene-containing plasmids between members of the 220…”

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confidence: 99%

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Widespread transfer of mobile antibiotic resistance genes within individual gut microbiomes revealed through bacterial Hi-C

Kent

Vill

Shi

et al. 2020

Preprint

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12The gut microbiome harbors a 'silent reservoir' of antibiotic resistance (AR) genes that is thought to 13 contribute to the emergence of multidrug-resistant pathogens through the process of horizontal gene 14 transfer (HGT). To counteract the spread of AR genes, it is paramount to know which organisms harbor 15 mobile AR genes and with which organisms they engage in HGT. Despite methods to characterize the 16 bulk presence 1 , abundance 2 and function 3 of AR genes in the gut, technological limitations of short-read 17sequencing have precluded linking bacterial taxa to specific mobile genetic elements (MGEs) and their 18 concomitant AR genes. Here, we apply and evaluate a high-throughput, culture-independent method for 19surveilling the bacterial carriage of MGEs, based on bacterial Hi-C protocols. We compare two healthy 20individuals with a cohort of seven neutropenic patients undergoing hematopoietic stem cell 21 transplantation, who receive multiple courses of antibiotics throughout their prolonged hospitalizations, 22and are thus acutely vulnerable to the threat of multidrug-resistant infections 4 . We find that the networks 23 of HGT are surprisingly distinct between individuals, yet AR and mobile genes are more dispersed across 24 taxa within the neutropenic patients than the healthy subjects. Our data further suggest that HGT is 25occurring throughout the course of treatment in the microbiomes of neutropenic patients and within the 26 guts of healthy individuals over a similar timeframe. Whereas most efforts to understand the spread of AR 27genes have focused on pathogenic species, our findings shed light on the role of the human gut 28 microbiome in this process. 29 preferentially recruited to contigs that are longer and more abundant, but to a lesser degree than expected, 81reducing potential bias in our dataset toward highly abundant organisms (Extended Data Figure 4). We 82 binned contigs using several tools (Maxbin 22 , MetaBat and Concoct), and applied a binning aggregation 83 strategy, DAS Tool 23 , to obtain a set of draft genomic assemblies. As misassembly can resemble HGT, we 84removed assemblies with greater than 10% contamination, as determined by CheckM, resulting in 85 taxonomically coherent assemblies (Extended Data Figure 5), albeit a greater number of unbinned contigs 86(24.6% of the total) (Extended Data Table 3). We then apply conservative criteria to link mobile and 87 mobile AR-containing contigs with the genomic draft assemblies, considering an MGE part of a genome 88 assembly only if it is directly linked to it by at least two uniquely-mapped Hi-C read-pairs. As MGEs are 89 known to recombine, this mitigates the potential for falsely linking contigs that merely share common 90 mobile genes. However, this also potentially reduces our ability for overall detection, especially for larger 91MGEs, since mobile contigs are often fragmented in metagenomic assemblies 24 . Nevertheless, we 92restricted our analysis to those AR-organism and MGE-organism linkages derived from high-confidence ...

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Sublethal Levels of Antibiotics Promote Bacterial Persistence in Epithelial Cells

Liu

Ding

et al. 2020

Advanced Science

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Antibiotic therapy and host cells frequently fail to eliminate invasive bacterial pathogens due to the emergence of antibiotic resistance, resulting in the relapse and recurrence of infections. Bacteria evolve various strategies to persist and survive in epithelial cells, a front‐line barrier of host tissues counteracting invasion; however, it remains unclear how bacteria hijack cellular responses to promote cytoplasmic survival under antibiotic therapy. Here, it is demonstrated that extracellular bacteria show invasive behavior and survive in epithelial cells in both in vivo and in vitro models, to increase antibiotic tolerance. In turn, sublethal levels of antibiotics increase bacterial invasion through promoting the production of bacterial virulence factors. Furthermore, antibiotic treatments interrupt lysosomal acidification in autophagy due to the internalized bacteria, using Bacillus cereus and ciprofloxacin as a model. In addition, it is found that sublethal levels of ciprofloxacin cause mitochondrial dysfunction and reactive oxygen species (ROS) accumulation to impair lysosomal vascular tape ATPase (V‐ATPase) to further promote bacterial persistence. Collectively, these results highlight the potential of host cells mediated antibiotic tolerance, which markedly compromises antibiotic efficacy and worsens the outcomes of infection.

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Salmonella persisters promote the spread of antibiotic resistance plasmids in the gut

Cited by 171 publications

References 39 publications

Estimating plasmid conjugation rates: a new computational tool and a critical comparison of methods

Estimating plasmid conjugation rates: a new computational tool and a critical comparison of methods

Widespread transfer of mobile antibiotic resistance genes within individual gut microbiomes revealed through bacterial Hi-C

Sublethal Levels of Antibiotics Promote Bacterial Persistence in Epithelial Cells

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