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，使用同时从所有样品中产生的两种不同的荧光染料进行相关性分析。这一计算是基于这样的期望，即在每次分析中，来自一个全长PCR片段的两个t - rf的比例几乎相同。该方法分析了唾液中的口腔微生物群落，利用支持向量机（SVM）从口腔微生物群落的表观丰度出发，根据峰值频率加权系数，预测了口腔空气中引起口腔异味的挥发性硫化合物甲基硫醇的浓度。利用该程序对220份人唾液样本进行口腔菌群分析，鉴定出24个菌群。用ISF加权的细菌群表观丰度在训练支持向量机中预测甲基硫醇在口腔异味阈值浓度以上的存在，并使用剩余的110个样本对结果进行测试。根据程序计算的细菌组成模式，预测甲基硫醇浓度的准确度为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%.