Background: Antimicrobial resistance (AMR) in Legionella pneumophila is emerging as a concern, particularly with resistance to macrolides and fluoroquinolones. Although clinically significant resistance in Legionella pneumophila remains uncommon, systematic genomic surveillance using whole-genome sequencing (WGS) is needed to anticipate treatment failure as metagenomic diagnostics move toward routine use. Objectives: We assessed the UK Health Security Agency AMR pipeline for predicting resistance in L. pneumophila by analysing 522 L. pneumophila isolates from England and Wales (2020–2023) together with nine database sequences that carry confirmed 23S rRNA mutations conferring high-level azithromycin resistance. The objective of the present study was to examine the presence of antimicrobial resistance genes (ARGs) in L. pneumophila isolates and to determine whether they exhibited phenotypic resistance through minimum inhibitory concentration (MIC) testing. Methods: Serogroups (sgs) were determined using an in-house qPCR assay, and L. pneumophila non-sg1 isolates were serogrouped using the Dresden monoclonal antibody (mAb) typing method. Sequence types were determined using the standard sequence-based typing method by Sanger sequencing. WGS reads were screened against standard AMR databases to identify resistance genes and resistance-mediating mutations. Agar dilution measured MICs for azithromycin, erythromycin, ampicillin, levofloxacin, tetracycline and spectinomycin in isolates possessing the bla_OXA-29, lpeAB or aph(9)-Ia gene. Results: AMR screening detected lpeAB, two allelic β-lactamase variants (bla_OXA-29 and bla_LoxA) and aph(9)-Ia in 165 of the 522 L. pneumophila isolates, while all high-azithromycin MIC reference sequences contained the expected 23S mutation. Only lpeAB was associated with a significant twofold elevation in macrolide MICs. Neither β-lactamase variant increased ampicillin MICs, and aph(9)-Ia carriage did not correlate with higher spectinomycin MICs. Conclusions: Advanced genomic analytics can now deliver timely therapeutic guidance, yet database-flagged genes may not translate into phenotypic resistance. Continuous pairing of curated mutation catalogues with confirmatory testing remains essential for distinguishing clinically actionable determinants such as 23S mutations and lpeAB from silent markers like bla_OXA-29 and aph (9)-Ia.

Background: Mycoplasma pneumoniae is a bacterium that causes upper and lower respiratory infections in children and adults. Epidemics occur over the winter period every few years with the most recent seen in 2023/24. These epidemics are thought to be due to the circulation of p1 type variants of M. pneumoniae which have cyclic epidemiology, with the peaks of infection driven by naïve individuals, mainly those < 5-years-old. Currently there is no routine monitoring or genomic surveillance to detect variants of M. pneumoniae circulating in the UK. We analysed 38 genomes of M. pneumoniae isolated in the UK between 2016 and 2024 for the detection of Macrolide-resistant M. pneumoniae (MRMP) and typing of M. pneumoniae variants. Core-genome analysis was used to compare the M. pneumoniae UK data with the global genomic data of 290 M. pneumoniae strains from 14 countries. Results: Of the 38 isolates from UK, the majority were obtained from male patients (62%), with the most common age range (38%) being the 0–15 years age group. All 38 UK isolates were identified as M. pneumoniae, of which 19 isolates (50%) were identified as p1-type 1 strains, and 19 isolates (50%) as p1-type 2. Of 38 isolates, six (16%) isolates were MRMP, of which four (67%) MRMP isolates were identified as sequence type (ST)3 from 2024 and the remaining two MRMP isolates from 2016 to 2024 were identified as ST14. Two core-genome phylogenetic trees; one based on strains from UK only (38 strains) and the other on a combined dataset of global and UK strains (328 strains), revealed two main clades corresponding to p1 types (p1-type 1 and p1-type 2 clades). Within these clades, strains were generally grouped into subclades according to their sequence type (ST), with a few exceptions observed in the global dataset. The phylogenetic tree for the 328 strains (290 global data and 38 UK data) showed 7 Subclades: Subclade 1 (ST3), Subclade 2 (mainly ST3/ST20), Subclade 3 (ST17), Subclade 4 (ST1), Subclade 5 (mainly ST14), Subclade 6 (mainly ST7/ST47) and Subclade 7 (ST2). ST3 strains were positioned in two Subclades (Subclade 1 and Subclade 2). 99% of the isolates in Subclade 1 (ST3) were MRMP. ST3 MRMP includes the most predominant strains in East Asia (Taiwan, Korea, Japan and China). Subclade 1 (ST3) was predominant in the strains isolated in 2024 from the UK that were included in this study out of which three ST3 MRMP strains were clustered in Subclade 1. Macrolide resistance was present in all Subclades except Subclade 7 (ST2). Conclusions: It is imperative to continuously monitor Subclade 1 in the UK to better understand the burden of macrolide resistance and the potential spread and evolution of this strain within the population. Whole genome sequencing based genomic surveillance of M. pneumoniae is a vital tool for the detection of macrolide resistance, and the surveillance of circulating strains during outbreaks and seasonal epidemic surges.