Bacteria spatial tracking in Urban Park soils with MALDI-TOF Mass Spectrometry and Specific PCR.

IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Biodata Mining Pub Date : 2023-01-14 DOI:10.1186/s13040-022-00318-6
Diego Arnal, Celeste Moya, Luigi Filippelli, Jaume Segura-Garcia, Sergi Maicas
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引用次数: 1

Abstract

Urban parks constitute one of the main leisure areas, especially for the most vulnerable people in our society, children, and the elderly. Contact with soils can pose a health risk. Microbiological testing is a key aspect in determining whether they are suitable for public use. The aim of this work is to map the spatial distribution of potential dangerous Enterobacteria but also bioremediation useful (lipase producers) isolates from soils in an urban park in the area of Valencia (Spain). To this end, our team has collected 25 samples of soil and isolated 500 microorganisms, using a mobile application to collect information of the soil samples (i.e. soil features, temperature, humidity, etc.) with geolocation. A combined protocol including matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) and 16S rDNA sequencing PCR has been established to characterize the isolates. The results have been processed using spatial statistical techniques (using Kriging method), taking into account the number of isolated strains, also proving the reactivity against standard pathogenic bacterial strains (Escherichia coli, Bacillus cereus, Salmonella, Pseudomonas and Staphylococcus aureus), and have increased the number of samples (to 896 samples) by interpolating spatially each parameter with this statistical method. The combined use of methods from biology and computer science allows the quality of the soil in urban parks to be predicted in an agile way, which can generate confidence in its use by citizens.

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利用MALDI-TOF质谱法和特异PCR技术追踪城市公园土壤细菌的空间分布。
城市公园构成了主要的休闲场所之一,特别是对于我们社会中最弱势的人群,儿童和老年人。与土壤接触会造成健康风险。微生物测试是决定它们是否适合公众使用的一个关键方面。这项工作的目的是绘制潜在危险肠杆菌的空间分布,以及从巴伦西亚(西班牙)地区一个城市公园的土壤中分离出的生物修复有用的(脂肪酶产生者)分离物。为此,我们团队采集了25个土壤样本,分离了500个微生物,使用移动应用程序收集土壤样本的地理定位信息(即土壤特征、温度、湿度等)。建立了基质辅助激光解吸/电离飞行时间质谱(MALDI-TOF MS)和16S rDNA测序PCR的联合方案来鉴定分离物。利用空间统计技术(Kriging法)对结果进行处理,考虑到分离菌株的数量,也证明了对标准病原菌(大肠杆菌、蜡样芽孢杆菌、沙门氏菌、假单胞菌和金黄色葡萄球菌)的反应性,并利用该统计方法对各参数进行空间插值,增加了样本数量(896个样本)。结合使用生物学和计算机科学的方法,可以以一种灵活的方式预测城市公园的土壤质量,这可以使市民对其使用产生信心。
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
Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
审稿时长
23 weeks
期刊介绍: BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
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