A novel PCR-based genotyping method for Proteus mirabilis – Intergenic region polymorphism analysis

IF 1.7 4区生物学 Q4 BIOCHEMICAL RESEARCH METHODS

Journal of microbiological methods Pub Date : 2024-08-03 DOI:10.1016/j.mimet.2024.107008

Nianqing Kong , Yilin Hu , Chenglu Lan , Shuilian Bi

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引用次数: 0

Abstract

Proteus mirabilis is a predominant species in cases of food poisoning associated with meat products and is also an opportunistic pathogen causing numerous infections in humans. This study aimed to differentiate P. mirabilis isolates using intergenic region polymorphism analysis (IRPA). The IRPA typing scheme was developed to amplify polymorphic fragments in intergenic regions (IGRs). The presence, absence, or size change of amplified products were identified and utilized as genetic markers for rapid differentiation of strains. A total of 75 P. mirabilis isolates were isolated from 63 fresh poultry and pork samples were subtyped using the IRPA and ERIC-PCR methods, and their antibiotic resistance profiles were tested. The majority of P. mirabilis isolates showed resistance to tetracycline (85.3%), doxycycline (93.3%), chloramphenicol (82.7%), streptomycin (92.0%), spectinomycin (80.0%), trimethoprim (97.3%); trimethoprim-sulfalleth (82.7%), and erythromycin (100.0%). In contrast, resistance rates to ceftriaxon, cefoxitin, cefepime, and cefotaxim were lower at only 17.3%, 5.3%, 6.7%, and 13.3%, respectively, among P. mirabilis isolates. Eleven loci were selected for analysis of the genetic diversity of 75 P. mirabilis isolates. A combination of 4 loci was determined as the optimal combination. The results compared to those obtained using ERIC-PCR for the same isolates. The Simpson's index of diversity was 0.999 for IRPA and 0.923 for ERIC-PCR, indicating that IRPA has a higher discriminatory power than ERIC-PCR. The concordance between IRPA and ERIC-PCR methods was low, primarily because IRPA classified isolates from the same ERIC cluster into separate clusters due to its high resolution. The IRPA method presented in this study offers a rapid, simple, reproducible, and economical approach for genotyping P. mirabilis.

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基于 PCR 的 mirabilis 变形杆菌基因分型新方法--基因间区多态性分析。

mirabilis 变形杆菌是与肉制品有关的食物中毒病例中的主要菌种，也是一种机会性病原体，可导致人类多种感染。本研究旨在利用基因间区多态性分析（IRPA）来区分奇异变形杆菌。IRPA分型方法用于扩增基因间区（IGR）的多态性片段。扩增产物的存在、缺失或大小变化被确定并用作快速区分菌株的遗传标记。利用 IRPA 和 ERIC-PCR 方法对从 63 个新鲜家禽和猪肉样本中分离出的 75 株奇异变形杆菌进行了亚型鉴定，并检测了它们的抗生素耐药性。大多数奇异变形杆菌分离株对四环素（85.3%）、强力霉素（93.3%）、氯霉素（82.7%）、链霉素（92.0%）、广谱霉素（80.0%）、三甲氧苄啶（97.3%）、三甲氧苄啶-磺胺嘧啶（82.7%）和红霉素（100.0%）具有耐药性。相比之下，奇异变形杆菌分离株对头孢曲松、头孢西丁、头孢吡肟和头孢他啶的耐药率较低，分别只有 17.3%、5.3%、6.7% 和 13.3%。选择了 11 个位点来分析 75 个 P. mirabilis 分离物的遗传多样性。4 个位点的组合被确定为最佳组合。结果与使用 ERIC-PCR 对相同分离物进行分析的结果进行了比较。IRPA的辛普森多样性指数为0.999，ERIC-PCR的辛普森多样性指数为0.923，表明IRPA比ERIC-PCR具有更高的鉴别力。IRPA 和 ERIC-PCR 方法的一致性较低，这主要是因为 IRPA 因其高分辨率而将来自同一 ERIC 聚类的分离物分为不同的聚类。本研究提出的 IRPA 方法为 mirabilis 杆菌的基因分型提供了一种快速、简单、可重复且经济的方法。

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来源期刊

Journal of microbiological methods 生物-生化研究方法

CiteScore

4.30

自引率

4.50%

发文量

151

审稿时长

29 days

期刊介绍： The Journal of Microbiological Methods publishes scholarly and original articles, notes and review articles. These articles must include novel and/or state-of-the-art methods, or significant improvements to existing methods. Novel and innovative applications of current methods that are validated and useful will also be published. JMM strives for scholarship, innovation and excellence. This demands scientific rigour, the best available methods and technologies, correctly replicated experiments/tests, the inclusion of proper controls, calibrations, and the correct statistical analysis. The presentation of the data must support the interpretation of the method/approach. All aspects of microbiology are covered, except virology. These include agricultural microbiology, applied and environmental microbiology, bioassays, bioinformatics, biotechnology, biochemical microbiology, clinical microbiology, diagnostics, food monitoring and quality control microbiology, microbial genetics and genomics, geomicrobiology, microbiome methods regardless of habitat, high through-put sequencing methods and analysis, microbial pathogenesis and host responses, metabolomics, metagenomics, metaproteomics, microbial ecology and diversity, microbial physiology, microbial ultra-structure, microscopic and imaging methods, molecular microbiology, mycology, novel mathematical microbiology and modelling, parasitology, plant-microbe interactions, protein markers/profiles, proteomics, pyrosequencing, public health microbiology, radioisotopes applied to microbiology, robotics applied to microbiological methods,rumen microbiology, microbiological methods for space missions and extreme environments, sampling methods and samplers, soil and sediment microbiology, transcriptomics, veterinary microbiology, sero-diagnostics and typing/identification.