Published in PLOS One on 2026-04-16, this study is presented as an attributed evidence briefing. The excerpts below are the original researchers’ words, reproduced under their Creative Commons Attribution licence. Health Signal has selected the abstract and, where needed, opening background paragraphs; the complete methods, results, limitations and references remain in the linked paper.
Original authors: William Boateng, Christian Owusu-Nyantakyi, Felicia Owusu, Grebstad R Amuasi, Quaneeta Mohktar, Pernille Nilsson, Bright Adu, Rene S Hendriksen, Beverly Egyir
Source: PLOS One • DOI 10.1371/journal.pone.0344837
Read the complete primary study ↗ · Creative Commons Attribution licence
From the researchers
Abstract
Gram-negative bacteria species cause increasing levels of antimicrobial resistance worldwide. Enhanced surveillance efforts are required to inform treatment decisions and monitoring of the rise and spread of antimicrobial resistant (AMR) clones, especially on the African continent, where antimicrobial resistance is known to be least tackled and controlled. In this study, whole genome sequencing was used to investigate a collection of Gram negatives recovered from clinical sources. Bacterial species were identified by Matrix-assisted Laser Desorption/Ionization Time of Flight mass spectrometry. Whole genome sequencing was performed using the Miseq illumina platform, and sequence data were analysed using free online bioinformatics tools. Of the 182 isolates investigated, 62 resistant to at least one antibiotic were selected for whole genome sequencing. Among these, Escherichia coli (n=21; 33.87%) and Klebsiella pneumoniae (n=13; 20.97%) were the predominant Enterobacterales, while Pseudomonas aeruginosa (9/16; 56.25%) was most common among non-Enterobacterales. The 62 Isolates sequenced were from wound (n=37), urine (n=19), blood (n=5), and pus (n=1). In total, 49 isolates were found to exhibit multidrug resistance (MDR). Genomic analysis revealed 126 resistance gene types, with beta-lactamase-encoding genes being the most common (56/126; 44.44%), detected in 90.32% (56/62) of organisms. K. pneumoniae (13/13; 100%) and Klebsiella oxytoca (1/1; 100%) exhibited coexisting OqxA and OqxB efflux pump genes. All E. coli isolates carried the MDR gene mdf(A). An Enterobacter kobei wound isolate carried the colistin resistance gene mcr-10. The quaternary ammonium compound resistance gene qacE was present in 50% (31/62) of isolates. Additionally, 41.94% (26/62) of isolates harbored the traT virulence gene.. High-risk clones detected included MDR ST131 E. coli serotype O25:H4 (6/21; 28.57%), ST15 and ST147 K. pneumoniae, and ST244 P. aeruginosa. Salmonella enterica serovars Lille and Typhi recovered from blood were also identifed. The study revealed high risk clones of Gram negatives carrying multiple AMR and virulence genes. The detection of MDR pathogens and global high-risk clones, highlights the need for effective surveillance and the use of whole genome sequencing to strengthen antimicrobial resistance monitoring in our setting.
Introduction
In recent times, numerous infections have posed challenges in treatment owing to the rise of antimicrobial resistance (AMR), a pressing global public health issue [1]. Gram-negative bacteria are medically important due to their ability to cause critical illnesses such as urinary tract infections, hospital-acquired pneumonia, bacteremia and intra-abdominal infections [2]. Of importance are the species: Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa and Acinetobacter baumannii which are among the leading causes of community and hospital acquired infections [3,4]. These pathogens are largely successful because they possess mechanisms that confer resistance to multiple classes of antibiotics and an array of virulence genes [5–7]. This includes the global emergence of resistance to last-line antimicrobials such as colistin and carbapenem stemming from improper usage of the drugs. The inappropriate use of antimicrobials has resulted in escalating selective pressures in both hospital and community settings, fostering genomic evolutions in these pathogens [8].
Reading this evidence
Use the full paper to check the study design, participant selection, comparator and outcome definitions before drawing a practical conclusion. An association does not by itself show cause and effect. A reported performance measure needs its denominator and uncertainty. A finding from a particular facility, sample or time period should retain that scope when it is discussed elsewhere.
This briefing makes the source accessible within the wider research & evidence desk. It does not assess an individual patient or certify a product. When considering relevance to another health system, examine the original authors’ account of context and limitations, then seek local implementation evidence. The related reporting below provides other studies to read alongside this one; it is a topic connection, rather than a claim that their results are directly comparable.
© 2026 William Boateng, Christian Owusu-Nyantakyi, Felicia Owusu, Grebstad R Amuasi, Quaneeta Mohktar, Pernille Nilsson, Bright Adu, Rene S Hendriksen, Beverly Egyir. Excerpts reproduced under CC BY. Selection, formatting and contextual notes by Health Signal. The researchers are the authors of the source study, not members of Health Signal’s staff.
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