利用宏基因组机器学习识别婴儿肠道微生物组受影响的抗性基因

Abstract: Antibiotic resistance in pathogens is extensively studied, and yet little is known about how antibiotic resistance genes of typical gut bacteria influence microbiome dynamics. Here, we leveraged genomes from metagenomes to investigate how genes of the premature infant gut resistome correspond to the ability of bacteria to survive under certain environmental and clinical conditions. We found that formula feeding impacts the resistome. Random forest models corroborated by statistical tests revealed that the gut resistome of formula-fed infants is enriched in class D beta-lactamase genes. Interestingly, Clostridium difficile strains harboring this gene are at higher abundance in formula-fed infants than C. difficile strains lacking this gene. Organisms with genes for major facilitator superfamily drug efflux pumps have higher replication rates under all conditions, even in the absence of antibiotic therapy. Using a machine learning approach, we identified genes that are predictive of an organism’s direction of change in relative abundance after administration of vancomycin and cephalosporin antibiotics. The most accurate results were obtained by reducing annotated genomic data to five principal components classified by boosted decision trees. Among the genes involved in predicting whether an organism increased in relative abundance after treatment are those that encode subclass B2 beta-lactamases and transcriptional regulators of vancomycin resistance. This demonstrates that machine learning applied to genome-resolved metagenomics data can identify key genes for survival after antibiotics treatment and predict how organisms in the gut microbiome will respond to antibiotic administration.

中文摘要：对病原体的抗生素耐药性进行了广泛的研究，但对典型的肠道细菌的抗生素抗性基因如何影响微生物组动力学知之甚少。在这里，我们利用来自宏基因组的基因组来调查早产儿肠道抗性组的基因如何对应于细菌在某些环境和临床条件下存活的能力。我们发现配方喂食会影响胶囊。通过统计学检验证实的随机森林模型显示，配方喂养的婴儿的肠道抗原组富含D类β-内酰胺酶基因。有趣的是，携带这种基因的艰难梭菌菌株在配方喂养的婴儿中比缺乏该基因的艰难梭菌菌株具有更高的丰度。具有主要促进剂超家族药物外排泵基因的生物在所有条件下都具有较高的复制速率，即使在没有抗生素治疗的情况下也是如此。使用机器学习方法，我们确定了预测万古霉素和头孢菌素类抗生素给药后相对丰度变化的方向的基因。通过将注释的基因组数据减少到由提高的决策树分类的五个主要组分来获得最准确的结果。在涉及预测治疗后生物体相对丰度是否增加的基因中，编码亚型B2β-内酰胺酶和万古霉素抗性转录调节因子的基因中。这表明应用于基因组解析的宏基因组学数据的机器学习可以确定抗生素治疗后存活的关键基因，并预测肠道微生物组中的生物体将如何响应抗生素施用。（Google翻译，未整理，供参考）

DOI: 10.1128/mSystems.00123-17