Université de Paris, IAME, INSERM, Paris, France.
AP-HP, Hôpital Bichat, Laboratoire de Bactériologie, Paris, France.
CPT Pharmacometrics Syst Pharmacol. 2022 Jul;11(7):906-918. doi: 10.1002/psp4.12806. Epub 2022 May 18.
Recent studies have highlighted the importance of ecological interactions in dysbiosis of gut microbiota, but few focused on their role in antibiotic-induced perturbations. We used the data from the CEREMI trial in which 22 healthy volunteers received a 3-day course of ceftriaxone or cefotaxime antibiotics. Fecal samples were analyzed by 16S rRNA gene profiling, and the total bacterial counts were determined in each sample by flux cytometry. As the gut exposure to antibiotics could not be experimentally measured despite a marked impact on the gut microbiota, it was reconstructed using the counts of susceptible Escherichia coli. The dynamics of absolute counts of bacterial families were analyzed using a generalized Lotka-Volterra equations and nonlinear mixed effect modeling. Bacterial interactions were studied using a stepwise approach. Two negative and three positive interactions were identified. Introducing bacterial interactions in the modeling approach better fitted the data, and provided different estimates of antibiotic effects on each bacterial family than a simple model without interaction. The time to return to 95% of the baseline counts was significantly longer in ceftriaxone-treated individuals than in cefotaxime-treated subjects for two bacterial families: Akkermansiaceae (median [range]: 11.3 days [0; 180.0] vs. 4.2 days [0; 25.6], p = 0.027) and Tannerellaceae (13.7 days [6.1; 180.0] vs. 6.2 days [5.4; 17.3], p = 0.003). Taking bacterial interaction as well as individual antibiotic exposure profile into account improves the analysis of antibiotic-induced dysbiosis.
最近的研究强调了生态相互作用在肠道微生物失调中的重要性，但很少关注它们在抗生素诱导的扰动中的作用。我们使用了 CEREMI 试验的数据，其中 22 名健康志愿者接受了为期 3 天的头孢曲松或头孢噻肟抗生素治疗。通过 16S rRNA 基因谱分析粪便样本，并通过通量细胞术确定每个样本中的总细菌计数。由于尽管抗生素对肠道微生物群有明显影响，但肠道暴露于抗生素仍无法通过实验测量，因此使用易感性大肠杆菌的计数来重建。使用广义Lotka-Volterra 方程和非线性混合效应模型分析细菌家族绝对计数的动态。使用逐步方法研究细菌相互作用。确定了两个负相互作用和三个正相互作用。在建模方法中引入细菌相互作用可以更好地拟合数据，并提供了与不考虑相互作用的简单模型不同的每个细菌家族的抗生素作用估计值。对于两个细菌家族，头孢曲松治疗个体恢复到基线计数 95%的时间明显长于头孢噻肟治疗个体：Akkermansiaceae（中位数[范围]：11.3[0；180.0] 天 vs. 4.2[0；25.6] 天，p=0.027）和 Tannerellaceae（13.7[6.1；180.0] 天 vs. 6.2[5.4；17.3] 天，p=0.003）。考虑细菌相互作用以及个体抗生素暴露情况可以改善对抗生素诱导的菌群失调的分析。
