Dataset Information

Detection of antimicrobial resistance in <5 h in Neisseria gonorrhoeae isolates using flow cytometry-proof of concept for seven clinically relevant antimicrobials.

ABSTRACT:

Introduction

Antimicrobial resistance in Neisseria gonorrhoeae compromises gonorrhoea treatment and rapid antimicrobial susceptibility testing (AST) would be valuable. We have developed a rapid and accurate flow cytometry method (FCM) for AST of gonococci.

Methods

The 2016 WHO gonococcal reference strains, and WHO Q, R and S (n = 17) were tested against seven clinically relevant antibiotics (ceftriaxone, cefixime, azithromycin, spectinomycin, ciprofloxacin, tetracycline and gentamicin). After 4.5 h incubation of inoculated broth, the fluorescent dye Syto™ 9 was added, followed by FCM analysis. After gating, the relative remaining population of gonococci, compared with unexposed growth control samples, was plotted against antimicrobial concentration, followed by non-linear curve regression analysis. Furthermore, the response at one single concentration/tested antibiotic was evaluated with the intention to use as a screening test for detection of resistant gonococci.

Results

A dose-dependent response was seen in susceptible isolates for all tested antimicrobials. There was a clear separation between susceptible/WT and resistant/non-WT isolates for ceftriaxone, cefixime, spectinomycin, ciprofloxacin and tetracycline. In contrast, for azithromycin, only high-level-resistant isolates were distinguished, while resistant isolates with MICs of 4 mg/L were indistinguishable from WT (MIC ≤ 1 mg/L) isolates. For gentamicin, all tested 17 isolates were WT and FCM analysis resulted in uniform dose-response curves. Using a single antibiotic concentration and a 50% remaining cell population cut-off, the overall sensitivity and specificity for resistance detection were 93% and 99%, respectively.

Conclusions

By providing results in <5 h for gonococcal isolates, FCM-based AST can become a rapid screening method for antimicrobial resistance or antimicrobial susceptibility in gonococci.

SUBMITTER: Somajo S

PROVIDER: S-EPMC10984928 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Detection of antimicrobial resistance in <5 h in Neisseria gonorrhoeae isolates using flow cytometry-proof of concept for seven clinically relevant antimicrobials.

Somajo Sofia S Nilsson Frida F Ekelund Oskar O Unemo Magnus M

The Journal of antimicrobial chemotherapy 20240401 4

<h4>Introduction</h4>Antimicrobial resistance in Neisseria gonorrhoeae compromises gonorrhoea treatment and rapid antimicrobial susceptibility testing (AST) would be valuable. We have developed a rapid and accurate flow cytometry method (FCM) for AST of gonococci.<h4>Methods</h4>The 2016 WHO gonococcal reference strains, and WHO Q, R and S (n = 17) were tested against seven clinically relevant antibiotics (ceftriaxone, cefixime, azithromycin, spectinomycin, ciprofloxacin, tetracycline and gentam ...[more]

PMID: 38334417

Similar Datasets

Project description:Cytometry experiments yield high-dimensional point cloud data that is difficult to interpret manually. Boolean gating techniques coupled with comparisons of relative abundances of cellular subsets is the current standard for cytometry data analysis. However, this approach is unable to capture more subtle topological features hidden in data, especially if those features are further masked by data transforms or significant batch effects or donor-to-donor variations in clinical data. We present that persistent homology, a mathematical structure that summarizes the topological features, can distinguish different sources of data, such as from groups of healthy donors or patients, effectively. Analysis of publicly available cytometry data describing non-naïve CD8+ T cells in COVID-19 patients and healthy controls shows that systematic structural differences exist between single cell protein expressions in COVID-19 patients and healthy controls. We identify proteins of interest by a decision-tree based classifier, sample points randomly and compute persistence diagrams from these sampled points. The resulting persistence diagrams identify regions in cytometry datasets of varying density and identify protruded structures such as 'elbows'. We compute Wasserstein distances between these persistence diagrams for random pairs of healthy controls and COVID-19 patients and find that systematic structural differences exist between COVID-19 patients and healthy controls in the expression data for T-bet, Eomes, and Ki-67. Further analysis shows that expression of T-bet and Eomes are significantly downregulated in COVID-19 patient non-naïve CD8+ T cells compared to healthy controls. This counter-intuitive finding may indicate that canonical effector CD8+ T cells are less prevalent in COVID-19 patients than healthy controls. This method is applicable to any cytometry dataset for discovering novel insights through topological data analysis which may be difficult to ascertain otherwise with a standard gating strategy or existing bioinformatic tools.

Dataset Information

Detection of antimicrobial resistance in <5 h in Neisseria gonorrhoeae isolates using flow cytometry-proof of concept for seven clinically relevant antimicrobials.

Introduction

Methods

Results

Conclusions

Publications

Detection of antimicrobial resistance in <5 h in Neisseria gonorrhoeae isolates using flow cytometry-proof of concept for seven clinically relevant antimicrobials.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets