AlShawaqfeh M K, Wajid B, Minamoto Y, Markel M, Lidbury J A, Steiner J M, Serpedin E, Suchodolski J S
Gastrointestinal Laboratory, Texas A&M University, College Station, TX 77843-4474, USA.
Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-4474, USA.
FEMS Microbiol Ecol. 2017 Nov 1;93(11). doi: 10.1093/femsec/fix136.
Recent studies have identified various bacterial groups that are altered in dogs with chronic inflammatory enteropathies (CE) compared to healthy dogs. The study aim was to use quantitative PCR (qPCR) assays to confirm these findings in a larger number of dogs, and to build a mathematical algorithm to report these microbiota changes as a dysbiosis index (DI). Fecal DNA from 95 healthy dogs and 106 dogs with histologically confirmed CE was analyzed. Samples were grouped into a training set and a validation set. Various mathematical models and combination of qPCR assays were evaluated to find a model with highest discriminatory power. The final qPCR panel consisted of eight bacterial groups: total bacteria, Faecalibacterium, Turicibacter, Escherichia coli, Streptococcus, Blautia, Fusobacterium and Clostridium hiranonis. The qPCR-based DI was built based on the nearest centroid classifier, and reports the degree of dysbiosis in a single numerical value that measures the closeness in the l2 - norm of the test sample to the mean prototype of each class. A negative DI indicates normobiosis, whereas a positive DI indicates dysbiosis. For a threshold of 0, the DI based on the combined dataset achieved 74% sensitivity and 95% specificity to separate healthy and CE dogs.
最近的研究已经确定，与健康犬相比，患有慢性炎症性肠病（CE）的犬体内有多种细菌群发生了改变。该研究的目的是使用定量聚合酶链反应（qPCR）检测方法在更多的犬只中证实这些发现，并构建一种数学算法，将这些微生物群的变化报告为失调指数（DI）。分析了95只健康犬和106只经组织学确诊为CE的犬的粪便DNA。样本被分为训练集和验证集。评估了各种数学模型和qPCR检测方法的组合，以找到具有最高鉴别力的模型。最终的qPCR检测组合包括八个细菌群：总细菌、粪杆菌属、Turicibacter、大肠杆菌、链球菌属、布劳特氏菌属、梭杆菌属和平贺氏梭菌。基于qPCR的DI是基于最近质心分类器构建的，并以单个数值报告失调程度，该数值测量测试样本在l2范数下与每个类别的平均原型的接近程度。负DI表示正常生物状态，而正DI表示失调。对于阈值0，基于合并数据集的DI在区分健康犬和CE犬时达到了74%的灵敏度和95%的特异性。
