Bioinformatics and Computational Biology Program, Iowa State University, 2014 Molecular Biology Building, Ames, IA, 50011, USA.
Department of Agricultural and Biosystems Engineering, Iowa State University, 1340 Elings Hall, 605 Bissell Road, Ames, IA, 50011, USA.
Commun Biol. 2022 Mar 17;5(1):216. doi: 10.1038/s42003-022-03155-9.
Effective monitoring of antibiotic resistance genes and their dissemination in environmental ecosystems has been hindered by the cost and efficiency of methods available for the task. We developed the Diversity of Antibiotic Resistance genes and Transfer Elements-Quantitative Monitoring (DARTE-QM), a method implementing TruSeq high-throughput sequencing to simultaneously sequence thousands of antibiotic resistant gene targets representing a full-spectrum of antibiotic resistance classes common to environmental systems. In this study, we demonstrated DARTE-QM by screening 662 antibiotic resistance genes within complex environmental samples originated from manure, soil, and livestock feces, in addition to a mock-community reference to assess sensitivity and specificity. DARTE-QM offers a new approach to studying antibiotic resistance in environmental microbiomes, showing advantages in efficiency and the ability to scale for many samples. This method provides a means of data acquisition that will alleviate some of the obstacles that many researchers in this area currently face.
有效监测环境生态系统中的抗生素耐药基因及其传播一直受到现有方法成本和效率的限制。我们开发了抗生素耐药基因和转移元件多样性-定量监测（DARTE-QM）方法，该方法采用 TruSeq 高通量测序技术，可同时对数千个抗生素耐药基因靶标进行测序，这些靶标代表了环境系统中常见的抗生素耐药类别的全谱。在这项研究中，我们通过对来自粪便、土壤和牲畜粪便的复杂环境样本以及模拟群落参考样本中的 662 个抗生素耐药基因进行筛选，证明了 DARTE-QM 的灵敏度和特异性。DARTE-QM 为研究环境微生物组中的抗生素耐药性提供了一种新方法，在效率和处理大量样本的能力方面具有优势。该方法提供了一种数据获取手段，可以缓解该领域许多研究人员目前面临的一些障碍。
