Hunter M Colby, Pozhitkov Alex E, Noble Peter A
Program in Microbiology, Alabama State University, Montgomery, AL 36101, United States.
Department of Oral Health, University of Washington, Box 3574444, Seattle, WA, United States.
J Microbiol Methods. 2016 Dec;131:85-101. doi: 10.1016/j.mimet.2016.09.019. Epub 2016 Oct 4.
Conceptual models suggest that certain microorganisms (e.g., the "red" complex) are indicative of a specific disease state (e.g., periodontitis); however, recent studies have questioned the validity of these models. Here, the abundances of 500+ microbial species were determined in 16 patients with clinical signs of one of the following oral conditions: periodontitis, established caries, edentulism, and oral health. Our goal was to determine if the abundances of certain microorganisms reflect dysbiosis or a specific clinical condition that could be used as a 'signature' for dental research. Microbial abundances were determined by the analysis of 138,718 calibrated probes using Gene Meter methodology. Each 16S rRNA gene was targeted by an average of 194 unique probes (n=25nt). The calibration involved diluting pooled gene target samples, hybridizing each dilution to a DNA microarray, and fitting the probe intensities to adsorption models. The fit of the model to the experimental data was used to assess individual and aggregate probe behavior; good fits (R>0.90) were retained for back-calculating microbial abundances from patient samples. The abundance of a gene was determined from the median of all calibrated individual probes or from the calibrated abundance of all aggregated probes. With the exception of genes with low abundances (<2 arbitrary units), the abundances determined by the different calibrations were highly correlated (r~1.0). Seventeen genera were classified as 'signatures of dysbiosis' because they had significantly higher abundances in patients with periodontitis and edentulism when contrasted with health. Similarly, 13 genera were classified as 'signatures of periodontitis', and 14 genera were classified as 'signatures of edentulism'. The signatures could be used, individually or in combination, to assess the clinical status of a patient (e.g., evaluating treatments such as antibiotic therapies). Comparisons of the same patient samples revealed high false negatives (45%) for next-generation-sequencing results and low false positives (7%) for Gene Meter results.
概念模型表明，某些微生物（如“红色复合体”）可指示特定的疾病状态（如牙周炎）；然而，最近的研究对这些模型的有效性提出了质疑。在此，我们测定了16名患有以下口腔疾病之一临床症状患者的500多种微生物的丰度：牙周炎、已确诊龋齿、无牙症和口腔健康。我们的目标是确定某些微生物的丰度是否反映了失调或一种可作为牙科研究“特征”的特定临床状况。微生物丰度通过使用基因计量方法分析138,718个校准探针来确定。每个16S rRNA基因平均由194个独特探针（n = 25nt）靶向。校准包括稀释混合的基因靶标样品，将每个稀释液与DNA微阵列杂交，并将探针强度拟合到吸附模型。模型与实验数据的拟合用于评估单个和总体探针行为；保留拟合良好（R> 0.90）的数据用于从患者样品中反推微生物丰度。基因的丰度由所有校准后的单个探针的中位数或所有聚集探针的校准丰度确定。除了低丰度基因（<2个任意单位）外，不同校准方法确定的丰度高度相关（r~1.0）。17个属被归类为“失调特征”，因为与健康状况相比，它们在牙周炎和无牙症患者中的丰度显著更高。同样，13个属被归类为“牙周炎特征”，14个属被归类为“无牙症特征”。这些特征可单独或组合使用，以评估患者的临床状况（如评估抗生素治疗等治疗方法）。对同一患者样本的比较显示，下一代测序结果的假阴性率较高（45%）而基因计量结果的假阳性率较低（7%）。
