Multi-locus sequence typing of Candida tropicalis among Candiduria shows an outbreak in azole-susceptible isolates and clonal cluster enriched in azole-resistant isolates

IF 3.9 3区医学 Q1 INFECTIOUS DISEASES

Journal of Hospital Infection Pub Date : 2025-02-01 DOI:10.1016/j.jhin.2024.11.018

L. Zhao , L.F. Xu , G.D. Xiang , Q.C. Zhou , Y. Wang , G.Y. Li

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引用次数: 0

Abstract

Background

The increasing detection rate of C. tropicalis and its azole resistance have made clinical treatment difficult. The presence of candiduria seems to correlate with invasive candida infection, especially for patients admitted to ICUs. However, the prevalence and antifungal resistance of C. tropicalis isolates in urine samples has not been well studied.

Aim

To retrospectively investigate the clinical features, antifungal resistance, and genetic relatedness of C. tropicalis isolates from urine samples.

Methods

A total of 107 clinical C. tropicalis isolates were retrospectively studied, including phenotypes of isolates and characteristics of patients. The genetic profiles of 107 isolates were genotyped using multi-locus sequence typing (MLST). Phylogenetic analysis was inferred using unweighted pair group method with arithmetic averages. MLST clonal clusters (CCs) were analysed by goeBURST.

Findings

Of the 107 isolates, 27.1% were resistant to fluconazole, and there was a notable increasing trend of fluconazole resistance from 16.1% in 2019 to 40.0% in 2021. Forty-seven diploid sequence types (DSTs) were assigned to ten major CCs. CC1 was the predominant fluconazole-susceptible group; 24 isolates from CC1 belonged to DST333, an outbreak clone in NICU ward. The azole-resistant CC4 contained 19 isolates, accounting for 65.5% of the azole-resistant isolates in this study. CC4 belongs to a prevalent FNS CC1 globally, of which the putative founder genotype was DST225.

Conclusion

This study revealed an outbreak of azole-susceptible C. tropicalis isolates in urine specimens and a high azole resistance rate of C. tropicalis in candiduria, and the MLST type showed clonal aggregation in azole-resistant isolates from urine samples.

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念珠菌间热带念珠菌多位点序列分型显示在唑敏感分离株中爆发，在唑耐药分离株中富集克隆簇。

背景：热带念珠菌的检出率不断上升，且对唑类药物产生抗药性，给临床治疗带来了困难。念珠菌尿的出现似乎与侵袭性念珠菌感染有关，尤其是对重症监护病房的患者而言。然而，关于尿液样本中分离出的热带念珠菌的流行率和抗真菌耐药性的研究并不多：回顾性研究尿液样本中分离出的热带真菌的临床特征、抗真菌耐药性和遗传相关性。采用多焦点序列分型法（MLST）对107株分离株进行基因分型。采用算术平均非加权成对分组法（UPGMA）进行了系统发育分析。用 goeBURST 分析了 MLST 克隆群（CC）：在107个分离株中，27.1%对氟康唑具有耐药性，氟康唑耐药性呈明显上升趋势，从2019年的16.1%上升到2021年的40.0%。47个二倍体序列类型（DST）被分配给10个主要的CC。CC1是主要的氟康唑耐药群体，CC1中的24个分离株属于DST333，这是NICU病房中的一个爆发性克隆。耐唑的 CC4 包含 19 个分离株，占本研究中耐唑分离株的 65.5%。CC4属于全球流行的FNS CC1，其推测的创始基因型为DST225：我们首次揭示了尿液标本中对唑类敏感的热带念珠菌分离株的爆发和念珠菌尿症中热带念珠菌的高唑耐药率，并且 MLST 类型显示尿液标本中唑耐药分离株的克隆聚集。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Hospital Infection 医学-传染病学

CiteScore

12.70

自引率

5.80%

发文量

271

审稿时长

19 days

期刊介绍： The Journal of Hospital Infection is the editorially independent scientific publication of the Healthcare Infection Society. The aim of the Journal is to publish high quality research and information relating to infection prevention and control that is relevant to an international audience. The Journal welcomes submissions that relate to all aspects of infection prevention and control in healthcare settings. This includes submissions that: provide new insight into the epidemiology, surveillance, or prevention and control of healthcare-associated infections and antimicrobial resistance in healthcare settings; provide new insight into cleaning, disinfection and decontamination; provide new insight into the design of healthcare premises; describe novel aspects of outbreaks of infection; throw light on techniques for effective antimicrobial stewardship; describe novel techniques (laboratory-based or point of care) for the detection of infection or antimicrobial resistance in the healthcare setting, particularly if these can be used to facilitate infection prevention and control; improve understanding of the motivations of safe healthcare behaviour, or describe techniques for achieving behavioural and cultural change; improve understanding of the use of IT systems in infection surveillance and prevention and control.