Paper reading (七十一)：Human Gut Microbiota Predicts Susceptibility to Vibrio cholerae Infection

论文题目：Human Gut Microbiota Predicts Susceptibility to Vibrio cholerae Infection

scholar 引用：7

页数：9

发表时间：2018.04

发表刊物：The Journal of Infectious Diseases

作者：Firas S Midani, Ana A Weil,..., Lawrence A David

摘要：

Background
Cholera is a public health problem worldwide, and the risk factors for infection are only partially understood.
Methods
We prospectively studied household contacts of patients with cholera to compare those who were infected to those who were not. We constructed predictive machine learning models of susceptibility, using baseline gut microbiota data. We identified bacterial taxa associated with susceptibility to Vibrio cholerae infection and tested these taxa for interactions with V. cholerae in vitro.
Results
We found that machine learning models based on gut microbiota, as well as models based on known clinical and epidemiological risk factors, predicted V. cholerae infection. A predictive gut microbiota of roughly 100 bacterial taxa discriminated between contacts who developed infection and those who did not. Susceptibility to cholera was associated with depleted levels of microbes from the phylum Bacteroidetes. By contrast, a microbe associated with cholera by our modeling framework, Paracoccus aminovorans, promoted the in vitro growth of V. cholerae. Gut microbiota structure, clinical outcome, and age were also linked.
Conclusion
These findings support the hypothesis that abnormal gut microbial communities are a host factor related to V. cholerae susceptibility.

正文组织架构：

1. Background

2. Materials and methods

2.1 Sample Collection, Classification of Outcomes, and 16S rRNA Analysis

2.2 Predictive Statistical Modeling of Outcomes

2.3 Spent Supernatant Culture Experiments

3. Results

3.1 V. cholerae Infection in Household Contacts of Patients With Cholera

3.2 Development of Machine Learning Model

3.3 Characteristics of Predictive Bacterial Taxa

3.4 Relationship Between Gut Microbiota Structure and Age

3.5 Association Between Microbes of Interest and V. cholerae In Vitro

4. Discussion 

正文部分内容摘录：

• 16S和ITS rRNA测序（内部转录间隔区核糖体RNA测序）是常用的扩增子测序方法，可用于鉴定和比较给定样本中的细菌或真菌。ITS和16S rRNA基因测序可比较来自复合微生物组或环境中难以研究甚至不可能研究的样本的系统发育和分类，它们都是该应用领域的成熟方法。
• OTU（operational taxonomic units），即操作分类单元。通过一定的距离度量方法计算两两不同序列之间的距离度量或相似性，继而设置特定的分类阈值，获得同一阈值下的距离矩阵，进行聚类操作，形成不同的分类单元。

1. Biological Problem: What biological problems have been solved in this paper?

• Human Gut Microbiota Predicts Susceptibility to Vibrio cholerae Infection 

2. Main discoveries: What is the main discoveries in this paper?

• We found that machine learning models based on gut microbiota, as well as models based on known clinical and epidemiological risk factors, predicted V. cholerae infection.
• A predictive gut microbiota of roughly 100 bacterial taxa discriminated between contacts who developed infection and those who did not. Susceptibility to cholera was associated with depleted levels of microbes from the phylum Bacteroidetes.
• By contrast, a microbe associated with cholera by our modeling framework, Paracoccus aminovorans, promoted the in vitro growth of V. cholerae. Gut microbiota structure, clinical outcome, and age were also linked.
• These findings support the hypothesis that abnormal gut microbial communities are a host factor related to V. cholerae susceptibility.
• Our models found cholera susceptibility to be characterized by depletion of bacteria normally found in healthy individuals. Members of the phylum Bacteroidetes, including 10 OTUs from the genus Prevotella, were among the taxa that our machine learning models identified in uninfected household contacts. 

3. ML(Machine Learning) Methods: What are the ML methods applied in this paper?

• We constructed predictive machine learning models of susceptibility, using baseline gut microbiota data.
• 多元逻辑回归分析来评估已知的影响霍乱易感性的临床和流行病学宿主因素:We used multivariate logistic regression to evaluate the clinical and epidemiologic host factors known to influence susceptibility to cholera (Stata, College Station, TX). Models were constructed using scikit-learn 0.17 in Python.
• In the hold-out model, we partitioned the data into a training set of 48 samples and a testing set of 28 samples.
• SVM来区分感染者和未感染者：We used a support vector machine (SVM) model that learned patterns of relative abundance of OTUs and distinguished infected from uninfected contacts.
• SVM结合RFE来删除无信息的细菌类群：We coupled the SVM to a recursive feature elimination (RFE) algorithm, which simplifies models and increases accuracy by removing uninformative bacterial taxa.
• 组合模型中，SVM应用于更多分类特征：For the combined model, an SVM was applied to age, blood group O status, vibriocidal antibody titer, and the OTUs selected by the microbiota-based model. We followed an identical procedure in the cross-validation model, using 30 replicates of 10-fold cross-validation.
• A total of 76 contacts formed our cohort for predicting cholera susceptibility. 
• We created a multivariate logistic regression model, using the known clinical and epidemiological risk factors for cholera in our cohort, to serve as a comparator for the microbial predictive models

4. ML Advantages: Why are these ML methods better than the traditional methods in these biological problems?

• AUC表现好？

5. Biological Significance: What is the biological significance of these ML methods’ results?

• This logistic regression model did not yield statistically significant results, likely owing to the small sample size. 没有统计学上的显著性差异？ the trends that we identified are consistent with findings from several larger cohorts
• These findings indicate that simple microbiota-based models were unable to predict cholera susceptibility. 简单的模型无法预测的
• Receiver operating characteristic curves showed that the SVM-RFE model built with microbiota data accurately predicted the clinical outcome among contacts (Figure 2A) and outperformed SVM-RFE models built with clinical and epidemiologic risk factors alone (area under the curve [AUC] = 0.80; P < .01). 我们提出的模型可以预测
• The optimal microbiota model used only 143 OTUs of the full set of 4181 input OTUs. A combined model using both microbiota and clinical data did not lead to improved performance (P > .05, by a 2-sided Mann-Whitney U test on the distribution of AUCs). AUC评价
• To learn more about specific bacterial taxa that influenced susceptibility to V. cholerae infection, we investigated the characteristics of the subset of gut microbes selected by our SVM-RFE algorithm. 研究了这个算法选择的特征

6. Prospect: What are the potential applications of these machine learning methods in biological science?

7. Mine Question(Optional)