Variability of strain engraftment and predictability of microbiome composition after fecal microbiota transplantation across different diseases.
Variability of strain engraftment and predictability of microbiome composition after fecal microbiota transplantation across different diseases.
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DOI：
10.1038/s41591-022-01964-3
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发表时间：
2022-09
期刊：
影响因子：
82.9
通讯作者：
Segata, Nicola
中科院分区：
文献类型：
--
作者：
Ianiro, Gianluca;Puncochar, Michal;Karcher, Nicolai;Porcari, Serena;Armanini, Federica;Asnicar, Francesco;Beghini, Francesco;Blanco-Miguez, Aitor;Cumbo, Fabio;Manghi, Paolo;Pinto, Federica;Masucci, Luca;Quaranta, Gianluca;De Giorgi, Silvia;Sciume, Giusi Desire;Bibbo, Stefano;Del Chierico, Federica;Putignani, Lorenza;Sanguinetti, Maurizio;Gasbarrini, Antonio;Valles-Colomer, Mireia;Cammarota, Giovanni;Segata, Nicola
粪便微生物群移植（FMT）对复发性艰难梭菌感染非常有效，被认为是其他微生物群相关疾病的有希望的治疗方法，但缺乏对微生物植入动力学的全面了解，这阻碍了这种治疗方法的明智应用。在这里，我们对新的和公开的粪便微生物组进行了综合的鸟枪宏基因组学系统荟萃分析，这些粪便微生物组收集自226个三元组的供体，前FMT受体和后FMT受体，跨越8种不同的疾病类型。通过利用改进的宏基因组菌株谱来推断菌株共享，我们发现当在研究中进行评估时，具有较高供体菌株植入的受体在FMT后更有可能经历临床成功（P = 0.017）。考虑到所有队列，与未经治疗的非传染性疾病患者相比，在接受多种途径FMT（例如，在同一治疗期间通过胶囊和结肠镜检查）的个体以及接受过免疫治疗的感染性疾病患者中观察到植入增加。除了六种特征不充分的厚壁菌门物种外，拟杆菌门和放线菌门物种（包括双歧杆菌）显示出比厚壁菌门更高的植入率。交叉数据集机器学习预测FMT后接受者中存在或不存在物种的平均AUROC为0.77，并强调了微生物丰度，流行率和分类学的相关性，以推断FMT后物种的存在。通过探索FMT后微生物组植入的动态及其与临床变量的关联，我们的研究揭示了物种特异性植入模式，并提出了能够预测供体的机器学习模型，这些模型可能优化FMT后特定微生物组特征，以用于疾病靶向FMT方案。将微生物宏基因组学与机器学习相结合，可以预测粪便微生物群移植（FMT）后的供体菌株植入，用于一系列疾病，并可以帮助定制FMT的设计，以优化微生物植入并实现临床结果。
Fecal microbiota transplantation (FMT) is highly effective against recurrent Clostridioides difficile infection and is considered a promising treatment for other microbiome-related disorders, but a comprehensive understanding of microbial engraftment dynamics is lacking, which prevents informed applications of this therapeutic approach. Here, we performed an integrated shotgun metagenomic systematic meta-analysis of new and publicly available stool microbiomes collected from 226 triads of donors, pre-FMT recipients and post-FMT recipients across eight different disease types. By leveraging improved metagenomic strain-profiling to infer strain sharing, we found that recipients with higher donor strain engraftment were more likely to experience clinical success after FMT (P = 0.017) when evaluated across studies. Considering all cohorts, increased engraftment was noted in individuals receiving FMT from multiple routes (for example, both via capsules and colonoscopy during the same treatment) as well as in antibiotic-treated recipients with infectious diseases compared with antibiotic-naïve patients with noncommunicable diseases. Bacteroidetes and Actinobacteria species (including Bifidobacteria) displayed higher engraftment than Firmicutes except for six under-characterized Firmicutes species. Cross-dataset machine learning predicted the presence or absence of species in the post-FMT recipient at 0.77 average AUROC in leave-one-dataset-out evaluation, and highlighted the relevance of microbial abundance, prevalence and taxonomy to infer post-FMT species presence. By exploring the dynamics of microbiome engraftment after FMT and their association with clinical variables, our study uncovered species-specific engraftment patterns and presented machine learning models able to predict donors that might optimize post-FMT specific microbiome characteristics for disease-targeted FMT protocols. Coupling microbial metagenomics with machine learning enables prediction of donor strain engraftment after fecal microbiota transplantation (FMT) for a range of diseases, and may help tailor design of FMT to optimize microbial engraftment and achieve clinical outcomes.
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影响因子：
7.7
作者：
Beghini F;McIver LJ;Blanco-Míguez A;Dubois L;Asnicar F;Maharjan S;Mailyan A;Manghi P;Scholz M;Thomas AM;Valles-Colomer M;Weingart G;Zhang Y;Zolfo M;Huttenhower C;Franzosa EA;Segata N
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作者：
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