Multiple plasmid origin-of-transfer substrates enable the spread of natural antimicrobial resistance to human pathogens

Abstract

Antimicrobial resistance poses a great danger to humanity, in part due to the widespread horizontal transfer of plasmids via conjugation. Modeling of plasmid transfer is essential to uncovering the fundamentals of resistance transfer and for development of predictive measures to limit the spread of resistance. However, a major limitation in the current understanding of plasmids is the inadequate characterization of the DNA transfer mechanisms, which conceals the actual potential for plasmid transfer in nature. Here, we consider that the plasmid-borne origin-of-transfer substrates encode specific DNA structural properties that can facilitate finding these regions in large datasets, and develop a DNA structure-based alignment procedure for typing the transfer substrates. Since our method outperforms current sequence-based approaches, we identify thousands of yet undiscovered DNA transfer substrates, showing that plasmid mobility is in fact 2-fold higher and spans almost 2-fold more host species than is currently known. Over half of all mobile plasmids contain the means to transfer between different mobility groups, which links previously confined host ranges across ecological habitats into a vast network of potential plasmid transfers. Certain types of conjugative transfer mechanisms and their corresponding hosts are transfer hubs that help break down the horizontal gene transfer barriers and form a robust gene flow system. We show that this network in fact serves to transfer resistance from the environmental genetic reservoirs to human MDR pathogens, which is an important driver of the observed rapid resistance development in humans and thus an important point of focus for future prevention measures.

Structural representations of DNA regulatory substrates can enhance sequence-based algorithms by associating functional sequence variants

“…We obtained 64 published DNA structure models that were shown to be informative for analysis of DNA-protein interactions [5] (Table 1). These models were based on nearest neighbor dinucleotide (56), trinucleotide (4) and pentanucleotide (4) models and included physicochemical and conformational properties and properties attributed to DNA-protein interactions.…”

“…The part of the transfer regions with relevant protein binding features from -140 bp to +80 bp according to the nicking site was used [3]. For testing the alignment algorithm, a second published dataset of 112 transfer regions (queries) of equal length as the ones above and 52 plasmids (targets) [5] from 4 Mob groups was used.…”

“…The statistical significance of distance scores (at p-value cutoffs from 1e-6 to 1e-1) was evaluated using bootstrap permutations (n = 1e6 per sequence) of 10 randomly selected query sequences (Eq. 7) and a mapping function between the distance scores and p-values was then obtained by least squares curve fitting (Matlab) to a second order polynomial function (distance score of 0 corresponded to the theoretical limit of 1e-132) [5].…”

“…1A, Methods M2). The calculated DNA structural properties included physicochemical, conformational and protein-DNA binding variables of experimental origin [5] (Table 1). Dimensionality reduction with Principal Component Analysis (PCA) yielded a specific number of principal components (PC) with each s-mer size s that explained over 99% of the data variance (Fig.…”

“…These are especially important in relation to interactions of the DNA with proteins, which drive many essential cellular processes [1,2,3]. These include but are not limited to transcription [1], replication [4] as well as horizontal gene transfer (HGT) [3,5]. The processes are commonly initiated and regulated at specific DNA regulatory regions, which are the substrates for protein binding and enzymatic activity, and include promoters [6], origins of replication [4] and origins of transfer (transfer regions, plasmid conjugation in HGT) [7].…”