摘要
出身背景
世界范围内抗微生物耐药性耐药性的负担是巨大的，而且还在不断增加。许多因素在抵抗力的出现中发挥了作用。我们的微生物组是抗微生物耐药性基因（ARGs）的重要库。抗菌药物的使用和滥用可以选择多重耐药细菌，并改变肠道微生物群中ARGs的库。有明确证据表明，住院的新冠肺炎患者改变了肠道微生物群，这可能会影响AMR的患病率和丰度。世界卫生组织（世卫组织）在全世界报告了3.64亿例病例。此外，美国疾病控制与预防中心宣布了7200万例阳性病例，仅在美国就有420多万例住院病例。
客观的
本研究的目的是回顾新冠肺炎（COVID-19）住院患者中AMG的流行情况，并探讨耐药组丰度的变化。
方法论
我们使用以下关键词搜索了美国国家生物技术中心数据库中的病原菌检测，以获取新冠肺炎（COVID-19）住院阳性病例，并提交了微生物组群测序报告：抗菌药物耐药性、抗生素管理、流行率、流行病学、耐药机制，以及新冠肺炎、SARS-CoV-2、细菌感染、住院、健康护理相关感染、，抗生素耐药性、抗微生物耐药性、多药耐药性、不动杆菌、克雷伯菌、链球菌、葡萄球菌、假单胞菌、大肠杆菌、肠球菌、耐甲氧西林金黄色葡萄球菌、新德里产金属-β-内酰胺酶的碳青霉烯耐药肠杆菌、严重急性呼吸综合征冠状病毒2型、万古霉素耐药肠球菌。基于网络的管道ResistoXplorer和Qiime2以及不同的数据库，包括RESfinder、Card和ARGminer，用于宏基因组学分析。它们主要基于编程语言JavaScript（版本：1.2.0）和R（版本：4.1.0）。
后果
我们的初步结果显示，从宣布新冠肺炎大流行到2022年1月，迄今为止，全世界共检测到11129株病毒。此外，分离株的流行率为鲍曼不动杆菌39.7%，其次是肺炎克雷伯菌32.2%，其次是大肠杆菌和志贺菌11.7%，肠杆菌5.4%，铜绿假单胞菌2.5%。我们探索了MRG的遗传多样性，包括：aac（6′）-Ib′，aadA1，和（3′′）-IIa，aph（3′）-Ib，apf（3′’）-Ia，app（6）-Id，armA，arr-2，blaADC-30，blaOXA-23，blaOXA-66，blaOXA。目前，我们正在进行WGS和16s测序分析，我们未来的工作包括丰度谱的数据分析，以及探索新冠肺炎阳性住院患者微生物群的AMR宏基因组学产生的耐药性特征。
结论
我们预计肠道微生物组将发生重大变化，因此新冠肺炎患者的ARG和耐药性丰富。
Abstract
Background
The burden of antimicrobial resistance AMR worldwide is substantial and is growing. Many factors play a role in the emergence of resistance. Our microbiome is a significant reservoir for antimicrobial resistance genes (ARGs). The use and misuse of antimicrobials can select multi-resistant bacteria and modify the repertoire of ARGs in the gut microbiota. There is clear evidence that hospitalized COVID patients have altered gut microbiota that might affect the prevalence and abundance of AMR. The World Health Organization (WHO) has reported 364 million cases worldwide. Moreover, the CDC announced 72 million positives with more than 4.2 million hospitalized cases in the US alone.

Objective
The purpose of the current study is to review AMGs prevalence among hospitalized COVID-19 patients with bacterial infections and explore the alteration of resistome abundance.

Methodology
We searched the pathogen detection of the National Center for Biotechnology database for hospitalized positive COVID-19 cases with microbiome sequencing submission using the following keywords: antimicrobial resistance, antibiotic stewardship, prevalence, epidemiology, mechanism of resistance, and COVID-19, SARS-CoV-2, bacterial infection, hospitalization, healthcare-associated infection, antibiotic resistance, antimicrobial resistance, multi-drug resistance, Acinetobacter, Klebsiella, Streptococcus, Staphylococcus, Pseudomonas, Escherichia, Enterococcus, Methicillin-resistant Staphylococcus aureus, New Delhi Metallo-β-lactamase-producing carbapenem-resistant enterobacterales, Severe acute respiratory syndrome coronavirus 2, Vancomycin-resistant enterococcus. Web-based pipelines ResistoXplorer and Qiime2 and different databases, including RESfinder, Card, and ARGminer, are used for the metagenomics analysis. They are mainly based on programming languages - JavaScript (version: 1.2.0) and R (version: 4.1.0).

Results
Our preliminary results showed that, to date, 11,129 isolates were detected worldwide between the declaration of the covid as a pandemic and January 2022. Moreover, the isolates prevalence was Acinetobacter baumannii at 39.7%, followed by Klebsiella pneumonia at 32.2%, then E.coli and Shigella at 11.7%, Enterobacter at 5.4%, and Pseudomonas aeruginosa 2.5%. Finally, infections such as Klebsiella oxytoca, Citrobacter freundii, and Providencia alcalifaciens were around 10%. We explored the genetic diversity of MRGs, including: aac(6′)-Ib′, aadA1, and(3′′)-IIa, aph(3′′)-Ib, aph(3′)-Ia, aph(6)-Id, armA, arr-2, blaADC-30, blaOXA-23, blaOXA-66, blaOXA. Currently, we are working on WGS and 16s sequencing analysis, and our future work includes data analysis on abundance profiles and exploring the resistome signatures generated from AMR metagenomics of the microbiota of positive COVID- hospitalized patients.

Conclusion
We are anticipating that there will be a significant change take place in the gut microbiome, hence the ARGs and resistome abundance in patients with COVID-19.

https://faseb.onlinelibrary.wiley.com/doi/abs/10.1096/fasebj.2022.36.S1.L7521