The Use of Machine Learning Methodologies to Analyse Antibiotic and Biocide Susceptibility in Staphylococcus aureus
The Use of Machine Learning Methodologies to Analyse Antibiotic and Biocide Susceptibility in Staphylococcus aureus
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DOI：
10.1371/journal.pone.0055582
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发表时间：
2013-02-19
期刊：
影响因子：
3.7
通讯作者：
Freitas, Ana Teresa
中科院分区：
文献类型：
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作者：
Coelho, Joana Rosado;Carrico, Joao Andre;Freitas, Ana Teresa
背景：病原菌对抗生素耐药性的增加是感染性疾病治疗中的一个重要问题。耐药性通常由抗生素本身选择;然而，杀生物剂也可能共同选择对抗生素的耐药性。尽管对杀生物剂的抗性定义不明确，但不同的体外研究表明，对杀生物剂表现出低敏感性的突变体也具有降低的对抗生素的敏感性。然而，与天然细菌分离株的研究是有限的，并没有明确的结论，是否使用杀菌剂的结果在开发的multidrug resistant bacterios.Methods：主要目标是执行一个公正的盲法为基础的评价抗生素和杀菌剂之间的关系减少敏感性的金黄色葡萄球菌的天然菌株。迄今为止研究的最大数据集之一，包括1632个人类临床分离的S。金黄色葡萄球菌来源于世界各地进行了分析。对所有菌株进行了13种抗生素和4种杀菌剂的表型表征。对杀生物剂和抗生素的敏感性降低之间的复杂联系很难在表型数据中使用标准统计方法来阐明。因此，机器学习技术应用于探索data.Results：在这项先驱性研究中，我们证明了减少敏感性两种常见的杀菌剂，洗必泰和苯扎氯铵，这属于不同的结构家族，与多药耐药性。我们一直发现，两种杀生物剂的最小抑菌浓度大于2 mg/L与S. aureus.Conclusions:我们的工作产生了两个重要结果，一个是方法学上的，另一个是与抗生素耐药性领域相关的。我们无法得出结论，抗生素的使用是否会选择杀菌剂耐药性，反之亦然。然而，观察到多重耐药性和两种常用的杀生物剂之间的关联可能是未来感染性疾病治疗的关注点。
Background: The rise of antibiotic resistance in pathogenic bacteria is a significant problem for the treatment of infectious diseases. Resistance is usually selected by the antibiotic itself; however, biocides might also co-select for resistance to antibiotics. Although resistance to biocides is poorly defined, different in vitro studies have shown that mutants presenting low susceptibility to biocides also have reduced susceptibility to antibiotics. However, studies with natural bacterial isolates are more limited and there are no clear conclusions as to whether the use of biocides results in the development of multidrug resistant bacteria.Methods: The main goal is to perform an unbiased blind-based evaluation of the relationship between antibiotic and biocide reduced susceptibility in natural isolates of Staphylococcus aureus. One of the largest data sets ever studied comprising 1632 human clinical isolates of S. aureus originated worldwide was analysed. The phenotypic characterization of 13 antibiotics and 4 biocides was performed for all the strains. Complex links between reduced susceptibility to biocides and antibiotics are difficult to elucidate using the standard statistical approaches in phenotypic data. Therefore, machine learning techniques were applied to explore the data.Results: In this pioneer study, we demonstrated that reduced susceptibility to two common biocides, chlorhexidine and benzalkonium chloride, which belong to different structural families, is associated to multidrug resistance. We have consistently found that a minimum inhibitory concentration greater than 2 mg/L for both biocides is related to antibiotic non-susceptibility in S. aureus.Conclusions: Two important results emerged from our work, one methodological and one other with relevance in the field of antibiotic resistance. We could not conclude on whether the use of antibiotics selects for biocide resistance or vice versa. However, the observation of association between multiple resistance and two biocides commonly used may be of concern for the treatment of infectious diseases in the future.