Monitoring the spread of Acinetobacter sp. in several at-risk units of the Alessandria Hospital, Mar 09 '23

The importance of antibiotic-resistance phenomenon and its worldwide spread have given rise to the activation of numerous surveillance systems. To make the data collected by these systems homogeneous and interpretable and to facilitate comparison between the various countries, a European surveillance network was created in 2000 which in 2010 assumed institutional characteristics becoming the European network European Antimicrobial Resisitance Surveillance Network (EARS-Net) coordinated by the ECDC.The aim of this work was to evaluate the antibiotic-resistance profile analyzing the whole genome of 24 A. baumannii strains isolated from different departments of Alessandria (Italy) hospital, in the Covid-19 era. It was a relevant project for determining the genomic proximity between strains isolated during the period considered, especially to trace their origin.Methods. The isolation of A. baumannii strains took place in the period of the Sars-CoV-2 pandemic. Genomic DNA was extracted from over-night cultures in Mueller-Hinton Broth, using the DNeasy UltraClean Microbial kit. The DNA was then quantified and shotgun libraries built using the Nextera XT DNA Library Prep kit. After product purification, the libraries were normalized and sequenced using MiSeq. The obtained sequences were bioinformatically analysed using different softwares: TrimmoMatic, PhRed, SPAdes, CheckM, Mash, Prodigal, EggNOG, FiloFlan, and RGI.Results. Here, we propose a new work-flow in order to map epidemic clusters at the hospital level. Furthermore, the proposed parameter of Mash Index, and the relative trashold of 0.0052, is reliable and validable in determining the similarity between strains and in monitoring their diffusion.

抗生素耐药性现象及其全球蔓延的重要性，推动了各类监测系统的启用。为使这些系统采集的数据具备同质性与可解释性，并便于各国间开展比对，欧盟于2000年建立了欧洲监测网络，该网络于2010年获得正式建制，成为由欧洲疾控中心（European Centre for Disease Prevention and Control, ECDC）协调的欧洲抗菌药物耐药性监测网络（European Antimicrobial Resistance Surveillance Network, EARS-Net）。

本研究旨在评估抗生素耐药性特征，分析新冠疫情期间从意大利亚历山德里亚医院不同科室分离的24株鲍曼不动杆菌（Acinetobacter baumannii, A. baumannii）的全基因组。本项目旨在明确所研究时期内分离菌株的基因组亲缘关系，尤其用于追踪其传播源头。

方法
鲍曼不动杆菌菌株的分离工作开展于新冠疫情（Sars-CoV-2 pandemic）期间。采用DNeasy UltraClean微生物试剂盒（DNeasy UltraClean Microbial kit），从穆勒-辛顿肉汤（Mueller-Hinton Broth）的过夜培养物中提取基因组DNA。随后对DNA进行定量，并利用Nextera XT DNA文库制备试剂盒（Nextera XT DNA Library Prep kit）构建鸟枪法测序文库。产物纯化后，对文库进行归一化处理，再使用MiSeq测序仪完成测序。所得序列通过多款软件开展生物信息学分析：TrimmoMatic、PhRed、SPAdes、CheckM、Mash、Prodigal、EggNOG、FiloFlan及RGI。

结果
本研究提出了一种可用于绘制医院层面暴发聚类图谱的全新工作流程。此外，所提出的Mash指数参数及0.0052的相对阈值，在判定菌株相似性与监测其传播扩散方面具备可靠性与可验证性。