Development of a high-performance T-RFLP system for oral flora analysis

我们开发了一种新的方法来计算多个T-RFLP样品的数据，通过估计T-RF组合，应用相关分析，使用两种不同的荧光染料同时产生的所有样品。该计算是基于预期来自一个全长PCR片段的两个T-RF的比例在每次分析中几乎相同。使用这种方法，口腔微生物在唾液中进行了分析，并使用支持向量机（SVM）的口腔微生物群落中的物种的表观丰度和加权系数的基础上的峰值频率预测的浓度甲基氯仿，挥发性硫化合物，导致口腔恶臭，在口腔空气中。应用该程序分析了220份人唾液中的菌群，共鉴定出24个菌群。在110个样本中，通过ISF加权的细菌群的表观丰度用于训练SVM以预测高于口腔恶臭阈值浓度的甲基苯丙胺的存在，并使用剩余的110个样本对结果进行测试。根据该程序计算的细菌组成模式，预测甲基苯丙胺的浓度，准确率为90%。

We developed a new approach for calculating data from multiple T-RFLP samples by estimating T-RF combinations, applying a correlation analysis using two different fluorescent dyes generated simultaneously from all samples. This calculation is based on the expectation that the proportions of two T-RFs from one full-length PCR fragment would be nearly the same in each analysis. Using this method, oral microflorae in saliva were analyzed and the concentrations of methyl mercaptan, a volatile-sulfur compound that causes oral malodor, in mouth air were predicted using a support vector machine (SVM) from the apparent abundances of species in oral microflora and weighted by coefficients based on the peak frequency. Using this program, the oral microflorae in 220 human saliva samples were analyzed, and 24 bacterial groups were identified. The apparent abundances of bacterial groups weighted by the ISF in 110 samples were used in train a SVM to predict the presence of methyl mercaptan above the threshold concentration of oral malodor, and the results were tested using the remaining 110 samples. Based on the bacterial composition patterns calculated by the program, the concentration of methyl mercaptan was predicted at an accuracy of 90%.