摘要

      背景：抗生素药物的选择压力是明智管理抗生素治疗方案的重要考虑因素。目前，治疗决策是基于粗略的假设，迫切需要建立一个更量化的知识库，以便能够预测个别抗生素对人体肠道微生物群和抵抗力的影响。
      结果：应用鸟枪法宏基因组技术，我们定量分析了接受预防性抗生素治疗的两组血液病患者的肠道菌群变化；一组在图宾根的一家医院接受环丙沙星治疗，另一组在科隆的一家医院接受复方新诺明治疗。分析这个丰富的纵向数据集，我们发现两个治疗组的肠道菌群多样性在相同程度上降低，而对肠道抵抗力的影响不同。在科隆队列中，我们观察到磺胺类抗生素耐药基因（ARGs）的相对丰度每累积限定日剂量的cotrimoxazole急剧增加148.1%，而在用环丙沙星治疗的Tübingen队列中则没有。通过多变量建模，我们发现个体基线微生物群、抵抗性和质粒多样性、肝/肾功能、同时用药，特别是抗病毒药物等因素影响抵抗性改变。值得注意的是，我们观察到两个治疗组对纤溶酶穹顶的不同影响。在用复方新诺明治疗的队列中，携带精氨酸的质粒的数量显著增加，而在用环丙沙星治疗的队列中则没有，这表明复方新诺明可能更有效地促进耐药性的传播。
      结论：我们的研究在发展预测个体抗菌素对人体微生物和耐药性影响的能力方面迈出了一步。我们的结果表明，要实现这一目标，需要整合个体基线微生物群、抵抗性和活动性状态以及其他个体患者因素。这种个人化的预测可能在未来增加患者的安全性，减少耐药性的蔓延。

Background: The selection pressure exercised by antibiotic drugs is an important consideration for the wise stewardship of antimicrobial treatment programs. Treatment decisions are currently based on crude assumptions, and there is an urgent need to develop a more quantitative knowledge base that can enable predictions of the impact of individual antibiotics on the human gut microbiome and resistome.

Results: Using shotgun metagenomics, we quantified changes in the gut microbiome in two cohorts of hematological patients receiving prophylactic antibiotics; one cohort was treated with ciprofloxacin in a hospital in Tübingen and the other with cotrimoxazole in a hospital in Cologne. Analyzing this rich longitudinal dataset, we found that gut microbiome diversity was reduced in both treatment cohorts to a similar extent, while effects on the gut resistome differed. We observed a sharp increase in the relative abundance of sulfonamide antibiotic resistance genes (ARGs) by 148.1% per cumulative defined daily dose of cotrimoxazole in the Cologne cohort, but not in the Tübingen cohort treated with ciprofloxacin. Through multivariate modeling, we found that factors such as individual baseline microbiome, resistome, and plasmid diversity; liver/kidney function; and concurrent medication, especially virostatic agents, influence resistome alterations. Strikingly, we observed different effects on the plasmidome in the two treatment groups. There was a substantial increase in the abundance of ARG-carrying plasmids in the cohort treated with cotrimoxazole, but not in the cohort treated with ciprofloxacin, indicating that cotrimoxazole might contribute more efficiently to the spread of resistance.

Conclusions: Our study represents a step forward in developing the capability to predict the effect of individual antimicrobials on the human microbiome and resistome. Our results indicate that to achieve this, integration of the individual baseline microbiome, resistome, and mobilome status as well as additional individual patient factors will be required. Such personalized predictions may in the future increase patient safety and reduce the spread of resistance.

       https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6749691/