Machine learning algorithm to characterize antimicrobial resistance associated with the International Space Station surface microbiome.
Machine learning algorithm to characterize antimicrobial resistance associated with the International Space Station surface microbiome.
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DOI：
10.1186/s40168-022-01332-w
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发表时间：
2022-08-24
期刊：
影响因子：
15.5
通讯作者：
中科院分区：
文献类型：
--
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抗生素耐药性（AMR）对地球上的人类健康具有不利影响，并且由于微重力，辐射和限制，特别是对于长距离太空旅行，它在其他环境中同样令人担忧，例如太空栖息地。国际空间站（ISS）是研究微生物多样性和与太空飞行有关的毒力的理想场所。本研究使用了微生物追踪-1（MT-1）项目期间生成的ISS霰弹枪宏基因组数据以及12个月内在8个不同地点的3次飞行中产生的宏基因组组装基因组（MAG）。本研究的目的是确定与226个可培养菌株的全基因组相关的AMR基因，21个鸟枪宏基因组序列，以及从ISS环境样品中检索的24个MAG，这些样品用单叠氮丙啶（PMA;活微生物）处理。我们使用深度学习模型分析了数据，使我们能够超越仅基于高DNA序列相似性的传统截止值，并扩展AMR基因的目录。我们在PMA处理样品中的结果显示，在最后一次飞行中，皮氏卡拉米氏菌（一种与人类尿路感染相关的细菌）的AMR占主导地位。对从MT-1项目中分离的226株纯菌株的分析揭示了来自许多菌株的数百种抗生素耐药基因，包括两个顶级物种，对应于布冈氏肠杆菌和蜡样芽孢杆菌菌株。计算预测的抗生素耐药性在这两个物种的实验验证，表现出高度的一致性。具体而言，纸片试验数据证实了这两种病原体对各种β-内酰胺抗生素的高耐药性。总的来说，我们的计算预测和验证分析证明了机器学习在发现宏基因组数据集中隐藏的AMR决定因素方面的优势，扩大了对ISS环境微生物组及其在人类中致病潜力的理解。视频摘要在线版本包含补充材料，可通过10. 1186/s40168-022-01332-w获取。
Antimicrobial resistance (AMR) has a detrimental impact on human health on Earth and it is equally concerning in other environments such as space habitat due to microgravity, radiation and confinement, especially for long-distance space travel. The International Space Station (ISS) is ideal for investigating microbial diversity and virulence associated with spaceflight. The shotgun metagenomics data of the ISS generated during the Microbial Tracking–1 (MT-1) project and resulting metagenome-assembled genomes (MAGs) across three flights in eight different locations during 12 months were used in this study. The objective of this study was to identify the AMR genes associated with whole genomes of 226 cultivable strains, 21 shotgun metagenome sequences, and 24 MAGs retrieved from the ISS environmental samples that were treated with propidium monoazide (PMA; viable microbes). We have analyzed the data using a deep learning model, allowing us to go beyond traditional cut-offs based only on high DNA sequence similarity and extending the catalog of AMR genes. Our results in PMA treated samples revealed AMR dominance in the last flight for Kalamiella piersonii, a bacteria related to urinary tract infection in humans. The analysis of 226 pure strains isolated from the MT-1 project revealed hundreds of antibiotic resistance genes from many isolates, including two top-ranking species that corresponded to strains of Enterobacter bugandensis and Bacillus cereus. Computational predictions were experimentally validated by antibiotic resistance profiles in these two species, showing a high degree of concordance. Specifically, disc assay data confirmed the high resistance of these two pathogens to various beta-lactam antibiotics. Overall, our computational predictions and validation analyses demonstrate the advantages of machine learning to uncover concealed AMR determinants in metagenomics datasets, expanding the understanding of the ISS environmental microbiomes and their pathogenic potential in humans. Video Abstract The online version contains supplementary material available at 10.1186/s40168-022-01332-w.
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