PDF(Pubmed)
Abstract：
Since the emergence of high-risk clones worldwide, constant investigations have been undertaken to comprehend the molecular basis that led to their prevalent dissemination in nosocomial settings over time. So far, the complex and multifactorial genetic traits of this type of epidemic clones have allowed only the identification of biomarkers with low specificity. A machine learning algorithm was able to recognize unequivocally a biomarker for early and accurate detection of Acinetobacter baumannii global clone 1 (GC1), one of the most disseminated high-risk clones. A support vector machine model identified the U1 sequence with a length of 367 nucleotides that matched a fragment of the moaCB gene, which encodes the molybdenum cofactor biosynthesis C and B proteins. U1 differentiates specifically between A. baumannii GC1 and non-GC1 strains, becoming a suitable biomarker capable of being translated into clinical settings as a molecular typing method for early diagnosis based on PCR as shown here. Since the metabolic pathways of Mo enzymes have been recognized as putative therapeutic targets for ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) pathogens, our findings highlight that machine learning can also be useful in knowledge gaps of high-risk clones and provides noteworthy support to the literature to identify relevant nosocomial biomarkers for other multidrug-resistant high-risk clones. IMPORTANCE A. baumannii GC1 is an important high-risk clone that rapidly develops extreme drug resistance in the nosocomial niche. Furthermore, several strains have been identified worldwide in environmental samples, exacerbating the risk of human interactions. Early diagnosis is mandatory to limit its dissemination and to outline appropriate antibiotic stewardship schedules. A region with a length of 367 bp (U1) within the moaCB gene that is not subjected to lateral genetic transfer or to antibiotic pressures was successfully found by a support vector machine model that predicts A. baumannii GC1 strains. At the same time, research on the group of Mo enzymes proposed this metabolic pathway related to the superbug\'s metabolism as a potential future drug target site for ESKAPE pathogens due to its central role in bacterial fitness during infection. These findings confirm that machine learning used for the identification of biomarkers of high-risk lineages can also serve to identify putative novel therapeutic target sites.
摘要：
自从全球出现高风险克隆以来，一直在进行不断的调查，以了解随着时间的推移，导致其在医院环境中普遍传播的分子基础。到目前为止,这种流行病克隆的复杂和多因素的遗传性状只允许鉴定低特异性的生物标志物。机器学习算法能够明确识别用于早期和准确检测鲍曼不动杆菌全球克隆1（GC1）的生物标志物。传播最多的高风险克隆之一。支持向量机模型鉴定了长度为367个核苷酸的U1序列，该序列与moaCB基因的片段相匹配，编码钼辅因子生物合成C和B蛋白。U1在鲍曼不动杆菌GC1和非GC1菌株之间特异性区分，如本文所示，成为一种合适的生物标志物，能够转化为临床环境，作为基于PCR的早期诊断的分子分型方法。由于Mo酶的代谢途径已被认为是ESKAPE（屎肠球菌，金黄色葡萄球菌,肺炎克雷伯菌,鲍曼不动杆菌,铜绿假单胞菌,和肠杆菌物种）病原体，我们的研究结果突出表明,机器学习在高危克隆的知识空白中也是有用的,并为文献提供了值得注意的支持,以确定其他多药耐药高危克隆的相关院内生物标志物.重要性鲍曼不动杆菌GC1是一种重要的高风险克隆，在医院利基中迅速发展出极端的耐药性。此外，在世界各地的环境样本中已经鉴定出几种菌株，加剧人类互动的风险。早期诊断是强制性的，以限制其传播并概述适当的抗生素管理时间表。通过预测鲍曼不动杆菌GC1菌株的支持向量机模型，成功发现了moaCB基因中长度为367bp（U1）的区域，该区域未进行侧向遗传转移或抗生素压力。同时,对Mo酶组的研究提出，这种与超级细菌代谢相关的代谢途径是ESKAPE病原体未来潜在的药物靶位点，因为它在感染期间的细菌适应性中起着重要作用。这些发现证实了用于识别高风险谱系的生物标志物的机器学习也可以用于识别推定的新型治疗靶位点。
