Predicting Neurodegenerative Disease Using Prepathology Gut Microbiota Composition: a Longitudinal Study in Mice Modeling Alzheimer's Disease Pathologies.
Predicting Neurodegenerative Disease Using Prepathology Gut Microbiota Composition: a Longitudinal Study in Mice Modeling Alzheimer's Disease Pathologies.
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使用病理前肠道微生物群组成预测神经退行性疾病：阿尔茨海默病病理模型小鼠的纵向研究。
DOI：
10.1128/spectrum.03458-22
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发表时间：
2023-03-06
影响因子：
3.7
通讯作者：
Cope, Emily K.
中科院分区：
文献类型：
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作者：
Borsom, Emily M.;Conn, Kathryn;Keefe, Christopher R.;Herman, Chloe;Orsini, Gabrielle M.;Hirsch, Allyson H.;Avila, Melanie Palma;Testo, George;Jaramillo, Sierra A.;Bolyen, Evan;Lee, Keehoon;Caporaso, J. Gregory;Cope, Emily K.
肠道微生物群-脑轴被怀疑有助于阿尔茨海默病（AD）的发展，阿尔茨海默病是一种以淀粉样蛋白-β斑块沉积、神经纤维缠结和神经炎症为特征的神经退行性疾病。为了评估肠道微生物群-脑轴在AD中的作用，我们表征了建模淀粉样变性和tau蛋白病的雌性3xTg-AD小鼠和野生型（WT）遗传对照的肠道微生物群。从4至52周每两周收集粪便样品，并在Illumina MiSeq上扩增16 S rRNA基因的V4区域并测序。从结肠和海马提取RNA，转化为cDNA，并使用逆转录酶定量PCR（RT-qPCR）测量免疫基因表达。使用QIIME 2计算多样性指标，并应用随机森林分类器来预测在预测小鼠基因型中重要的细菌特征。24周时，结肠中胶质细胞酸性蛋白（GFAP;表明星形细胞增多）的基因表达升高。Th 1炎症（il 6）和小胶质细胞增生（mrc 1）的标记物在海马中升高。3xTg-AD小鼠和WT小鼠在生命早期的肠道微生物群组成不同（排列多变量方差分析[PERMANOVA]，8周，P = 0.001，24周，P = 0.039，和52周，P = 0.058）。使用粪便微生物组组成正确预测小鼠基因型的时间为90%至100%。最后，我们表明，拟杆菌物种的相对丰度随着时间的推移在3xTg-AD小鼠中增加。综上所述，我们证明了在病理学前时间点细菌肠道微生物群组成的变化是AD病理学发展的预测。重要性最近的研究已经证明了阿尔茨海默病（AD）病理模型小鼠肠道微生物群组成的改变;然而，这些研究仅包括多达4个时间点。我们的研究是第一次对转基因AD小鼠模型的肠道微生物群进行表征，每两周一次，从4周龄到52周龄，以量化与疾病病理学发展和宿主免疫基因表达相关的微生物组成的时间动态。在这项研究中，我们观察到特定微生物类群（包括拟杆菌属）相对丰度的时间变化，这些类群可能在疾病进展和病理严重程度中发挥核心作用。使用微生物群的特征在病理学前时间点区分AD模型小鼠和野生型小鼠的能力表明肠道微生物群作为AD的风险或保护因素的潜在作用。
The gut microbiota-brain axis is suspected to contribute to the development of Alzheimer’s disease (AD), a neurodegenerative disease characterized by amyloid-β plaque deposition, neurofibrillary tangles, and neuroinflammation. To evaluate the role of the gut microbiota-brain axis in AD, we characterized the gut microbiota of female 3xTg-AD mice modeling amyloidosis and tauopathy and wild-type (WT) genetic controls. Fecal samples were collected fortnightly from 4 to 52 weeks, and the V4 region of the 16S rRNA gene was amplified and sequenced on an Illumina MiSeq. RNA was extracted from the colon and hippocampus, converted to cDNA, and used to measure immune gene expression using reverse transcriptase quantitative PCR (RT-qPCR). Diversity metrics were calculated using QIIME2, and a random forest classifier was applied to predict bacterial features that are important in predicting mouse genotype. Gene expression of glial fibrillary acidic protein (GFAP; indicating astrocytosis) was elevated in the colon at 24 weeks. Markers of Th1 inflammation (il6) and microgliosis (mrc1) were elevated in the hippocampus. Gut microbiota were compositionally distinct early in life between 3xTg-AD mice and WT mice (permutational multivariate analysis of variance [PERMANOVA], 8 weeks, P = 0.001, 24 weeks, P = 0.039, and 52 weeks, P = 0.058). Mouse genotypes were correctly predicted 90 to 100% of the time using fecal microbiome composition. Finally, we show that the relative abundance of Bacteroides species increased over time in 3xTg-AD mice. Taken together, we demonstrate that changes in bacterial gut microbiota composition at prepathology time points are predictive of the development of AD pathologies. IMPORTANCE Recent studies have demonstrated alterations in the gut microbiota composition in mice modeling Alzheimer’s disease (AD) pathologies; however, these studies have only included up to 4 time points. Our study is the first of its kind to characterize the gut microbiota of a transgenic AD mouse model, fortnightly, from 4 weeks of age to 52 weeks of age, to quantify the temporal dynamics in the microbial composition that correlate with the development of disease pathologies and host immune gene expression. In this study, we observed temporal changes in the relative abundances of specific microbial taxa, including the genus Bacteroides, that may play a central role in disease progression and the severity of pathologies. The ability to use features of the microbiota to discriminate between mice modeling AD and wild-type mice at prepathology time points indicates a potential role of the gut microbiota as a risk or protective factor in AD.
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影响因子：
4.7
作者：
Chen Z;Shi K;Liu X;Dai Y;Liu Y;Zhang L;Du X;Zhu T;Yu J;Fang S;Li F
通讯作者：
Li F
影响因子：
6.8
作者：
Chatterjee P;Pedrini S;Stoops E;Goozee K;Villemagne VL;Asih PR;Verberk IMW;Dave P;Taddei K;Sohrabi HR;Zetterberg H;Blennow K;Teunissen CE;Vanderstichele HM;Martins RN
通讯作者：
Martins RN
影响因子：
15.5
作者：
Cope EK;Goldberg AN;Pletcher SD;Lynch SV
通讯作者：
Lynch SV
影响因子：
48
作者：
Callahan BJ;McMurdie PJ;Rosen MJ;Han AW;Johnson AJ;Holmes SP
通讯作者：
Holmes SP
影响因子：
14
作者：
通讯作者：
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