Search bioRxiv⌕ Search

bioRxiv · 10.64898/2026.10.14.759075

Intubation and mechanical ventilation lead to an extensive expansion of VAP-associated bacterial species in respiratory secretions of ventilated pigs

Abstract

Ventilator associated pneumonia (VAP) remains one of the most critical nosocomial infections in intensive care medicine. Between 20-50% of ventilated patients develop VAP during treatment, often leading to long term sequelae and death, despite successful therapy of their underlying disease. VAP is typically caused by Gram-negative bacteria that can previously be detected on the mucosal membranes of the oral and upper respiratory tract. To assess and quantify bacterial colonization of the respiratory tract following intubation and ventilation a well-established pig model was used. Methods: After induction of anaesthesia and endotracheal intubation pigs underwent mechanical ventilation continuously for up to three days. To assess bacterial colonization of the upper respiratory tract, samples of respiratory secretions were taken at day 0, 1 and 2 directly above the cuff outside of the endotracheal tube (ETT) and by aspiration of tracheal secretions through the ETT. Quantification of bacteria was carried out by serial dilutions and CFU/ml determinations on sheep blood agar and MacConkey agar plates. Species identification was achieved by 16S-RNA gene sequencing. Results: Respiratory samples of 19 pigs were analysed and showed a sharp increase of predominantly Gram-negative organisms in the respiratory material sampled above the cuff from day 0 to 1. Starting at a median of 7.3 x 103 immediately after intubation numbers increased to a median of 2.0 x 107 Gram-negative bacteria at day 1 and 3.2 x 107 at day 2. Gram-negative species that were detected included Pseudomonas aeruginosa, Acinetobacter baumanii, Enterobacter aerogenes, Klebsiella pneumoniae and other Enterobacteriaceae. Tracheal secretions contained much less bacteria. While no Gram-negative bacteria could be cultured at the beginning of the experiment in 18 of 19 animals, colonization with Gram-negative bacteria gradually increased and was found in 7 of 19 and 9 of 14 animals on day 1 and day 2 respectively. Interestingly, in the vast majority of colonized tracheal secretions the identical Gram-negative species had previously been detected in secretions collected > 24h earlier at the cuff. Conclusion: A massive increase of the concentration of Gram-negative bacteria occurred at 24h of ventilation in mucosal secretions above the cuff. Detected species were the same Gram-negative bacteria that are frequently responsible for human infections, suggesting that especially the accumulation of bacteria in secretions above the cuff may be crucial in the development of VAP.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Spring, L., Haider, D., Hoffmann, A., Zink, F., Hessling, M., Radermacher, P., Münz, F., Sharon, O., Scheer, M., Feth, M., Kapapa, T., Spellerberg, B.. 2026-10-14. Intubation and mechanical ventilation lead to an extensive expansion of VAP-associated bacterial species in respiratory secretions of ventilated pigs. https://doi.org/10.64898/2026.10.14.759075

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

A droplet-to-data workflow for antimicrobial screening of complex matrices

Screening plant extracts and other complex natural matrices for antibacterial activity is complicated by optical interference and population averaging in conventional bulk assays. Droplet-based assays mitigate interference while enabling high-throughput assessment of bacterial subpopulations. However, analyzing the resulting image data involves difficult image processing, classification, and interpretation. Here, we present a democratized workflow for antimicrobial screening in complex matrices that combines a polydisperse droplet-based assay with EasyFlow 2.0 (EF2), our updated droplet image analysis platform. EF2 offers stable containerized deployment, expanded polydisperse droplet support, automated thresholding and MIC estimation, LLM-assisted data exploration, and import of other software outputs such as CellProfiler (CP) measurements and CellProfiler Analyst (CPA) classification models. As proof of concept, we applied this workflow to assess the antibacterial and aggregation-modulating effects of chemically distinct extracts of Taraxacum officinale, Rhodiola rosea, and Lonicera caerulea against Escherichia coli. Extracts were chemically characterized by HPLC-MS and evaluated using both standard 96-well plate microdilution and droplet-based assays. In total, 27,059 droplets were segmented in CP, classified into four growth phenotypes using a CPA random forest model, and analyzed in EF2. The droplet-based assay reached minimal inhibitory concentration (MIC) at lower extract concentrations than 96-well plate microdilution and revealed extract-specific aggregation effects: L. caerulea reduced E. coli aggregation, whereas T. officinale did not. R. rosea reduced growth in the 96-well plate microdilution assay, but high concentrations caused droplet gelation that prevented droplet-based analysis, showing that sample components must be checked for compatibility with droplet formation. These results illustrate that the EF2-centered workflow supports rapid, automated MIC estimation through unsupervised thresholding when labels are unavailable, and four-class aggregation phenotyping when a CPA model is supplied. This workflow offers flexible, scalable screening, potentially extendable beyond plant extracts to other complex sample matrices.

microbiology↗

The influence of drug dosing regimen on extinction and adaptation during experimental evolution in Candida albicans

The antifungal azole drug fluconazole (FLC) is frequently prescribed for treating Candida albicans infections. Azole drugs are typically fungistatic; they inhibit cell growth rather than kill susceptible cells within a population. Therefore, fungal populations can adapt to fluconazole through increased drug resistance or increased drug tolerance (the ability of a subpopulation of susceptible cells to grow slowly at inhibitory drug concentrations). To our knowledge, no prior experiments have examined how different factors influence extinction propensity, although previous in vitro studies show that both drug concentration and strain background can influence adaptation. To determine how the drug dosing regimen, namely fluconazole concentration and transfer duration (i.e., the amount of time between transfer into fresh medium), influenced extinction and adaptation in Candida albicans, we conducted a fully factorial series of short in vitro evolution experiments. Dosing regimen significantly influenced extinction: all replicates evolved at all drug concentrations survived the 48 h and 72 h transfer experiments, yet the extinction rate increased sharply from low to high drug concentration with 24 h transfers. Increases in drug tolerance were frequent regardless of the dosing regimen. In contrast, drug resistance and growth rate both tended to decrease following evolution at higher drug concentrations. Fluconazole-evolved replicates exhibited frequent increases in cross-tolerance and decreases in cross-resistance in a panel of other azole drugs. Genome size changes were common in some strain backgrounds evolved to higher fluconazole concentrations, regardless of transfer times. Combined, our results indicate that many experimental factors can influence the trajectory of adaptation, and show that extinction in a fungistatic drug occurs readily at high drug concentrations with rapid transfers.

microbiology↗

Abundance-weighted analysis and deep functional metagenomic selection reveals unrecognized diversity of soil-derived streptothricin acetyltransferases

Streptothricins (STCs) are broad-spectrum antibiotics under investigation for their therapeutic potential against antibiotic-resistant Gram-negative pathogens. However, the extent and diversity of genes that confer resistance to STCs, or their environmental resistome, remains largely unknown and is essentially limited to a handful of STC acetyltransferases. Here, we used a deep functional metagenomic approach to uncover previously unrecognized STC resistance determinants. A functional metagenomic library containing ca. 700 Gigabases of soil metagenomic DNA was selected for resistance to nourseothricin (NTC), a mixture of STC congeners, resulting in collection and sequencing of nearly 30,000 resistant clones. An abundance-weighted sequence analysis identified 265 acetyltransferase-like candidates spanning broad phylogenetic, taxonomic, and genomic diversity. Re-cloning and experimental testing of 18 candidates chosen to cover acetyltransferase sequence space confirmed that 10 conferred significant NTC resistance in Escherichia coli. Phylogenetic analyses identified three previously uncharacterized STC acetyltransferase lineages, designated as SatC, SatD, and SatE. Despite substantial sequence divergence from characterized streptothricin acetyltransferases (maximum amino acid identities of approximately 31% for SatC and SatD and up to 47.5% for SatE), these enzymes largely retained key residues associated with streptothricin binding and catalysis. In addition to strongly increasing NTC resistance, expression of the SatC construct in E. coli also significantly decreased susceptibility to the aminoglycoside antibiotics amikacin and kanamycin. Together, these findings reveal a substantially broader than previously known environmental repertoire of STC acetyltransferases and demonstrate how deep functional metagenomic selections, combined with abundance-weighted hit calling, phylogenetic analysis, and structural prediction, can uncover novel resistance functions.

microbiology↗