摘要
      金黄色葡萄球菌是一种可导致严重疾病并对多种抗菌药物产生耐药性的病原体。它是ESKAPE生物的一部分，已被美国疾病控制与预防中心（CDC）列入对人类的严重威胁名单。许多抗微生物机制已经被确定，特别是抗微生物耐药性基因（ARGs）可以通过全基因组测序来确定。移动遗传元件（MGE）可以决定这些ARG在菌株和物种之间的传播，并可以通过生物信息学分析进行鉴定。这项工作的范围是分析可公开获得的金黄色葡萄球菌基因组，以表征染色体和质粒中存在的ARGs的发生及其地理分布、分离来源、克隆复合物和随时间的变化。结果表明，从29679个金黄色葡萄球菌基因组中，鉴定出24765条含有73个不同ARG的染色体，以及21006个含有47个不同ARGs的质粒重叠群。染色体中最丰富的ARG是mecA（84%），而blaZ在质粒重叠群中最丰富（30%），尽管它在染色体中也很丰富（42%）。共分配了13个克隆复合体，并强调了各大洲ARGs和CC分布的差异。过去20年（2001年至2020年）的时间变化表明，在质粒中，耐甲氧西林金黄色葡萄球菌和大环内酯类耐药性的发生率降低，而与氨基糖苷类耐药性相关的ARGs的发生率增加。尽管在所分析的大约一半基因组中缺乏元数据信息，但所获得的结果使人们能够通过设计和实施一个相对简单的管道来深入分析ARGs和MGE在不同类别中的分布，该管道也可以应用于未来与其他病原体的合作中，用于监测和筛查目的。
Abstract
Staphylococcus aureus is a pathogen that can cause severe illness and express resistance to multiple antimicrobial agents. It is part of the ESKAPE organisms and it has been included by the Centers for Disease Control and Prevention (CDC) of USA in the list of serious threats to humans. Many antimicrobial mechanisms have been identified, and, in particular, antimicrobial resistance genes (ARGs) can be determined by whole genome sequencing. Mobile genetic elements (MGEs) can determine the spread of these ARGs between strains and species and can be identified with bioinformatic analyses. The scope of this work was to analyse publicly available genomes of S. aureus to characterise the occurrence of ARGs present in chromosomes and plasmids in relation to their geographical distribution, isolation sources, clonal complexes, and changes over time. The results showed that from a total of 29,679 S. aureus genomes, 24,765 chromosomes containing 73 different ARGs, and 21,006 plasmidic contigs containing 47 different ARGs were identified. The most abundant ARG in chromosomes was mecA (84%), while blaZ was the most abundant in plasmidic contigs (30%), although it was also abundant in chromosomes (42%). A total of 13 clonal complexes were assigned and differences in ARGs and CC distribution were highlighted among continents. Temporal changes during the past 20 years (from 2001 to 2020) showed that, in plasmids, MRSA and macrolide resistance occurrence decreased, while the occurrence of ARGs associated with aminoglycosides resistance increased. Despite the lack of metadata information in around half of the genomes analysed, the results obtained enable an in-depth analysis of the distribution of ARGs and MGEs throughout different categories to be undertaken through the design and implementation of a relatively simple pipeline, which can be also applied in future works with other pathogens, for surveillance and screening purposes.

https://www.mdpi.com/2079-6382/11/11/1632