de la Cruz Barron, M. | Search bioRxiv

bioRxiv2026

Plasmid-plasmid interactions reshape extracellular vesicle cargo and gene transfer potential

Plasmids are key drivers of horizontal gene transfer, yet their dissemination is not limited to conjugation. Extracellular vesicles (EVs) can transport plasmid DNA, but the factors governing plasmid incorporation into EVs remain poorly understood. Here, we tested whether principles of conjugative plasmid transfer, including plasmid mobility type and plasmid-plasmid interactions, extend to EV-mediated export. Using a conjugative plasmid (pKJK5) and a mobilizable plasmid (RSF1010) in two Gram-negative hosts, we quantified plasmid incorporation into EVs under single- and dual-plasmid conditions. When present individually, the conjugative plasmid was preferentially incorporated into EVs, exceeding RSF1010 by 10-23-fold despite its lower intracellular abundance. Under co-residence, this pattern reversed: RSF1010 became enriched by 13-39-fold, while pKJK5 was reduced by 2-7-fold. Consequently, EV-associated plasmid cargo shifted to RSF1010 dominance, deviating strongly from the expected 10-fold higher pKJK5 cargo if a stochastic model based on intracellular abundance and single-plasmid conditions were applicable. We propose that mobilizable plasmids under coexistence exploit conjugative plasmid transfer machinery to access membrane-associated sites, increasing their likelihood of incorporation into EVs. Our findings demonstrate that plasmid-plasmid interactions reshape EV cargo and identify a previously unrecognized mechanism that may influence extracellular gene transfer potential in microbial communities.

microbiology↗

bioRxiv2025

Suppressing selection for antibiotic resistance in the environment: A transparent, ecology-based approach to predicted no-effect concentrations

Selection for antibiotic resistance has been demonstrated at low, environmentally-relevant antibiotic concentrations. Over the past decade, the concept of minimum selective concentrations (MSC) has been adopted in environmental regulation to define maximum permissible antibiotic concentrations. Such empirically determined MSC values often fail to reflect the complexity of natural communities, where susceptibility and resistance-associated fitness costs vary widely across species. To address this limitation, computational approaches have been developed to predict no-effect concentrations for selection of antibiotic resistance (PNECres) from routinely collected minimum inhibitory concentration (MIC) data. However, these approaches often lack a strong ecological basis, undermining confidence in their predictions. Here, we propose a simple but biologically consistent framework to derive PNECres values by integrating MIC data with probabilistic estimates of resistance-related fitness costs. Our results suggest that current regulatory environmental threshold concentrations should be lowered by at least one order of magnitude to guard against selection for antibiotic resistance.

microbiology↗

bioRxiv2022

Quantification of the mobility potential of antibiotic resistance genes through multiplexed ddPCR linkage analysis

Antibiotic resistance genes (ARGs) are widely disseminated within microbiomes of the human, animal, and environmental spheres. There is a clear need for global monitoring and risk assessment initiatives to evaluate the risks of ARGs towards human health. Therefore, not only ARG abundances within a given environment, but also their mobility, hence their ability to spread to human pathogenic bacteria needs to be quantified. Consequently, methods to accurately quantify the linkage of ARGs with mobile genetic elements are urgently needed. We developed a novel, sequencing-independent method for assessing ARG mobility by combining multiplexed droplet digital PCR (ddPCR) on DNA sheared into short fragments with statistical analysis. This allows quantifying the physical linkage between ARGs and mobile genetic elements, here demonstrated for the sulfonamide ARG sul1 and the Class1 integron integrase gene intI1. The methods efficiency is demonstrated using mixtures of model DNA fragments with either linked and unlinked target genes: Linkage of the two target genes can be accurately quantified based on high correlation coefficients between observed and expected values (R2) as well as low mean absolute errors (MAE) for both target genes, sul1 (R2=0.9997, MAE=0.71%, n=24) and intI1 (R2=0.9991, MAE=1.14%, n=24). Furthermore, we demonstrate that the chosen fragmentation length of DNA during shearing allows adjusting the rate of false positives and false negative detection of linkage. The applicability of the developed method for environmental samples is further demonstrated by assessing the mobility of sul1 across a wastewater treatment plant. The presented method allows rapidly obtaining reliable results within hours. It is labor- and costefficient and does not rely on sequencing technologies. Furthermore, it has a high potential to be scaled up to multiple targets. Consequently, it merits consideration to be included within global AMR surveillance initiatives for assessing ARG mobility. Author AbstractAntibiotic resistance represents a major problem in treating bacterial infections and endangers public health. Antibiotic resistant genes (ARGs) can spread among microbes in humans, animals, and the environment, thanks to mobile genetic elements. These are genetic structures involved in the mobility of genetic information and hence microbial traits. Methods that allow simultaneously quantifying the abundance of ARGs together with their association/linkage with mobile genetic elements are fundamental for assessing the risk they pose to human health, as mobility increases their likelihood to spread to human pathogens. Here we developed a novel method that allows to quantify the abundance and the linkage between ARGs and mobile genetic elements. The method relies on droplet digital PCR (ddPCR) technology performed on fragmented environmental DNA (of a chosen size), combined with statistical analysis. We found that the method accurately quantifies the linkage between the two targets in model and environmental DNA. The method is delivering rapid, labor- and cost-efficient results as it does not rely on technology that require prior bioinformatics knowledge and can be included in future monitoring frameworks.

microbiology↗