Shawrob Kazi Shefaul Mulk, Dhariwal Achal, Salvadori Gabriela, Gladstone Rebecca A, Junges Roger
Institute of Oral Biology, Faculty of Dentistry, University of Oslo, Oslo, Norway.
The Intervention Centre, Oslo University Hospital, Oslo, Norway.
Microb Genom. 2025 Jul;11(7). doi: 10.1099/mgen.0.001444.
is a leading pathogen in terms of deaths attributable to or associated with antimicrobial resistance globally. Thus, monitoring antibiotic resistance determinants constitutes a key aspect of surveillance efforts for this microbe. Leveraging publicly available whole-genome sequencing (WGS) data, we aimed to investigate the presence and distribution patterns of antibiotic resistance determinants in with a focus on multidrug resistance (MDR) and serotype distribution. Metadata and genomes were obtained from the National Center for Biotechnology Information Pathogen Detection database. Curation and harmonization were performed in R and SPSS. Data on resistance patterns were defined according to AMRFinderPlus, and a combination of prediction tools was employed for serotyping. Analyses involved 75,161 genomes totalling 122,673 gene/allele counts from 14 antibiotic classes. MDR was observed in 16.7% of isolates, with the highest increasing rates in Asia and South America. Within antibiotic classes, an increase in macrolide resistance genes was highlighted, particularly in the proportion of genomes presenting )/). Over a third of isolates with serotypes 19F, 23F, 15A, 6B and 19A showed MDR. We further observed the highest significant increases in the presence of resistance in 33F, 22F, 10A and 23A. Serotype 13, not included in any vaccine formulation, presented high MDR rates with a strong increasing trend. The findings of this study highlight variations in resistance determinants globally and across serotypes over time. Collectively, these data underscore the added value of utilizing public WGS data to investigate the effectiveness and repercussions of treatment and vaccination strategies on managing antibiotic resistance.
就全球因抗菌药物耐药性导致或与之相关的死亡而言，它是一种主要病原体。因此，监测抗生素耐药性决定因素是针对这种微生物的监测工作的一个关键方面。利用公开可用的全基因组测序（WGS）数据，我们旨在调查[具体微生物名称未给出]中抗生素耐药性决定因素的存在和分布模式，重点关注多重耐药性（MDR）和血清型分布。元数据和基因组从美国国家生物技术信息中心病原体检测数据库获得。在R和SPSS中进行整理和协调。根据AMRFinderPlus定义耐药模式数据，并采用多种预测工具组合进行[具体微生物名称未给出]血清分型。分析涉及来自14种抗生素类别的75161个基因组，共计122673个基因/等位基因计数。在16.7%的分离株中观察到多重耐药性，在亚洲和南美洲的增长率最高。在抗生素类别中，大环内酯类耐药基因的增加尤为突出，特别是在呈现[具体基因情况未给出]的基因组比例方面。超过三分之一的血清型19F、23F、15A、6B和19A分离株表现出多重耐药性。我们还进一步观察到血清型33F、22F、10A和23A中耐药性的显著增加最为明显。血清型13未包含在任何疫苗配方中，呈现出高多重耐药率且有强劲的上升趋势。本研究结果突出了全球范围内以及不同血清型随时间推移耐药性决定因素的变化。总体而言，这些数据强调了利用公共全基因组测序数据来研究治疗和疫苗接种策略在管理抗生素耐药性方面的有效性和影响的附加价值。
