Taguer M, Darbinian E, Wark K, Ter-Cheam A, Stephens D A, Maurice C F
Department of Microbiology & Immunology, Faculty of Medicine and Health Sciences, McGill Universitygrid.14709.3b, Montreal, Quebec, Canada.
Department of Mathematics and Statistics, Faculty of Science, McGill Universitygrid.14709.3b, Montreal, Quebec, Canada.
mSystems. 2021 Dec 21;6(6):e0050721. doi: 10.1128/mSystems.00507-21. Epub 2021 Dec 7.
Longitudinal studies on the gut microbiome that follow the effect of a perturbation are critical in understanding the microbiome's response and succession to disease. Here, we use a dextran sodium sulfate (DSS) mouse model of colitis as a tractable perturbation to study how gut bacteria change their physiology over the course of a perturbation. Using single-cell methods such as flow cytometry, bioorthogonal noncanonical amino acid tagging (BONCAT), and population-based cell sorting combined with 16S rRNA sequencing, we determine the diversity of physiologically distinct fractions of the gut microbiota and how they respond to a controlled perturbation. The physiological markers of bacterial activity studied here include relative nucleic acid content, membrane damage, and protein production. There is a distinct and reproducible succession in bacterial physiology, with an increase in bacteria with membrane damage and diversity changes in the translationally active fraction, both, critically, occurring before symptom onset. Large increases in the relative abundance of were seen in all physiological fractions, most notably in the translationally active bacteria. Performing these analyses within a detailed, longitudinal framework determines which bacteria change their physiology early on, focusing therapeutic efforts in the future to predict or even mitigate relapse in diseases like inflammatory bowel diseases. Most studies on the gut microbiome focus on the composition of this community and how it changes in disease. However, how the community transitions from a healthy state to one associated with disease is currently unknown. Additionally, common diversity metrics do not provide functional information on bacterial activity. We begin to address these two unknowns by following bacterial activity over the course of disease progression, using a tractable mouse model of colitis. We find reproducible changes in gut bacterial physiology that occur before symptom onset, with increases in the proportion of bacteria with membrane damage, and changes in community composition of the translationally active bacteria. Our data provide a framework to identify possible windows of intervention and which bacteria to target in microbiome-based therapeutics.
追踪扰动影响的肠道微生物组纵向研究对于理解微生物组对疾病的反应和演替至关重要。在此,我们使用葡聚糖硫酸钠(DSS)诱导的小鼠结肠炎模型作为一种易于处理的扰动,来研究肠道细菌在扰动过程中如何改变其生理状态。通过使用诸如流式细胞术、生物正交非经典氨基酸标记(BONCAT)等单细胞方法,以及基于群体的细胞分选结合16S rRNA测序,我们确定了肠道微生物群生理上不同部分的多样性以及它们对可控扰动的反应。此处研究的细菌活性生理标记包括相对核酸含量、膜损伤和蛋白质产生。细菌生理状态存在明显且可重复的演替,膜损伤细菌增加,翻译活性部分的多样性发生变化,这两者均在症状出现之前就已关键地发生。在所有生理部分中均观察到[具体细菌名称未给出]相对丰度大幅增加,最显著的是在翻译活性细菌中。在详细的纵向框架内进行这些分析,可确定哪些细菌在早期改变其生理状态,从而为未来预测甚至减轻炎症性肠病等疾病的复发提供治疗重点。大多数关于肠道微生物组的研究集中在该群落的组成及其在疾病中的变化。然而,目前尚不清楚该群落如何从健康状态转变为与疾病相关的状态。此外,常见的多样性指标无法提供关于细菌活性的功能信息。我们通过使用易于处理的结肠炎小鼠模型,在疾病进展过程中追踪细菌活性,开始解决这两个未知问题。我们发现肠道细菌生理状态在症状出现之前发生了可重复的变化,膜损伤细菌比例增加,翻译活性细菌的群落组成发生变化。我们的数据提供了一个框架,以确定可能的干预窗口以及在基于微生物组的治疗中靶向哪些细菌。