Rapid and accurate detection of Shiga toxin-producing Escherichia coli (STEC) serotype O157 : H7 by mass spectrometry directly from the isolate, using 10 potential biomarker peaks and machine learning predictive models.

IF 2.4 4区 医学 Q3 MICROBIOLOGY Journal of medical microbiology Pub Date : 2023-05-01 DOI:10.1099/jmm.0.001675
Eduardo Manfredi, María Florencia Rocca, Jonathan Zintgraff, Lucía Irazu, Elizabeth Miliwebsky, Carolina Carbonari, Natalia Deza, Monica Prieto, Isabel Chinen
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Abstract

Introduction. The different pathotypes of Escherichia coli can produce a large number of human diseases. Surveillance is complex since their differentiation is not easy. In particular, the detection of Shiga toxin-producing Escherichia coli (STEC) serotype O157 : H7 consists of stool culture of a diarrhoeal sample on enriched and/or selective media and identification of presumptive colonies and confirmation, which require a certain level of training and are time-consuming and expensive.Hypothesis. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is a quick and easy way to obtain the protein spectrum of a microorganism, identify the genus and species, and detect potential biomarker peaks of certain characteristics.Aim. To verify the usefulness of MALDI-TOF MS to rapidly identify and differentiate STEC O157 : H7 from other E. coli pathotypes.Methodology. The direct method was employed, and the information obtained using Microflex LT platform-based analysis from 60 clinical isolates (training set) was used to detect differences between the peptide fingerprints of STEC O157 : H7 and other E. coli strains. The protein profiles detected laid the foundations for the development and evaluation of machine learning predictive models in this study.Results. The detection of potential biomarkers in combination with machine learning predictive models in a new set of 142 samples, called 'test set', achieved 99.3 % (141/142) correct classification, allowing us to distinguish between the isolates of STEC O157 : H7 and the other E. coli group. Great similarity was also observed with respect to this last group and the Shigella species when applying the potential biomarkers algorithm, allowing differentiation from STEC O157 : H7Conclusion. Given that STEC O157 : H7 is the main causal agent of haemolytic uremic syndrome, and based on the performance values obtained in the present study (sensitivity=98.5 % and specificity=100.0 %), the implementation of this technique provides a proof of principle for MALDI-TOF MS and machine learning to identify biomarkers to rapidly screen or confirm STEC O157 : H7 versus other diarrhoeagenic E. coli in the future.

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利用10个潜在生物标志物峰和机器学习预测模型,通过质谱法直接从分离物中快速准确地检测产志贺毒素大肠杆菌(STEC)血清型O157: H7。
介绍。大肠杆菌的不同致病型可引起大量的人类疾病。监测是复杂的,因为它们不容易区分。特别是,产志贺毒素的大肠杆菌(STEC)血清型O157: H7的检测包括在浓缩和/或选择性培养基上对腹泻样本进行粪便培养,并对假定菌落进行鉴定和确认,这需要一定程度的培训,耗时且昂贵。基质辅助激光解吸/电离飞行时间质谱法(MALDI-TOF MS)是一种快速简便的获取微生物蛋白质谱、鉴定微生物属和种、检测具有某些特征的潜在生物标志物峰的方法。目的验证MALDI-TOF质谱在快速鉴定和区分STEC O157: H7与其他大肠杆菌病理型中的有效性。采用直接法,利用Microflex LT平台对60株临床分离株(训练集)进行分析,检测STEC O157: H7与其他大肠杆菌的肽指纹图谱差异。检测到的蛋白质谱为本研究中机器学习预测模型的开发和评估奠定了基础。结合机器学习预测模型对142个新样本(称为“测试集”)的潜在生物标志物的检测,实现了99.3%(141/142)的正确分类,使我们能够区分STEC O157: H7和其他大肠杆菌群的分离株。在应用潜在生物标记物算法时,还观察到最后一组和志贺氏菌种具有很大的相似性,可以从产志贺毒素大肠杆菌O157: h7v中区分出来。鉴于STEC O157: H7是溶血性尿毒症综合征的主要致病因子,并且基于本研究获得的性能值(灵敏度= 98.5%,特异性= 100.0%),该技术的实施为MALDI-TOF质谱和机器学习识别生物标志物提供了原理证明,以便在未来快速筛选或确认STEC O157: H7与其他腹泻性大肠杆菌。
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来源期刊
Journal of medical microbiology
Journal of medical microbiology 医学-微生物学
CiteScore
5.50
自引率
3.30%
发文量
143
审稿时长
4.5 months
期刊介绍: Journal of Medical Microbiology provides comprehensive coverage of medical, dental and veterinary microbiology, and infectious diseases. We welcome everything from laboratory research to clinical trials, including bacteriology, virology, mycology and parasitology. We publish articles under the following subject categories: Antimicrobial resistance; Clinical microbiology; Disease, diagnosis and diagnostics; Medical mycology; Molecular and microbial epidemiology; Microbiome and microbial ecology in health; One Health; Pathogenesis, virulence and host response; Prevention, therapy and therapeutics
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