Karlsson Lovisa, Öhrnberg Isabelle, Sayyab Shumaila, Martínez-Enguita David, Gustafsson Mika, Espinoza Patricia, Méndez-Aranda Melissa, Ugarte-Gil Cesar, Diero Lameck, Tonui Ronald, Paues Jakob, Lerm Maria
Division of Inflammation and Infection, Department of Biomedical and Clinical Sciences.
Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden.
J Infect Dis. 2025 Feb 4;231(1):e47-e58. doi: 10.1093/infdis/jiae333.
Tuberculosis (TB) is among the largest infectious causes of death worldwide, and there is a need for a time- and resource-effective diagnostic methods. In this novel and exploratory study, we show the potential of using buccal swabs to collect human DNA and investigate the DNA methylation (DNAm) signatures as a diagnostic tool for TB.
Buccal swabs were collected from patients with pulmonary TB (n = 7), TB-exposed persons (n = 7), and controls (n = 9) in Sweden. Using Illumina MethylationEPIC array, the DNAm status was determined.
We identified 5644 significant differentially methylated CpG sites between the patients and controls. Performing the analysis on a validation cohort of samples collected in Kenya and Peru (patients, n = 26; exposed, n = 9; control, n = 10) confirmed the DNAm signature. We identified a TB consensus disease module, significantly enriched in TB-associated genes. Last, we used machine learning to identify a panel of 7 CpG sites discriminative for TB and developed a TB classifier. In the validation cohort, the classifier performed with an area under the curve of 0.94, sensitivity of 0.92, and specificity of 1.
In summary, the result from this study shows clinical implications of using DNAm signatures from buccal swabs to explore new diagnostic strategies for TB.
结核病是全球最大的传染性死因之一,因此需要一种高效省时且资源有效的诊断方法。在这项新颖的探索性研究中,我们展示了使用口腔拭子收集人类DNA并研究DNA甲基化(DNAm)特征作为结核病诊断工具的潜力。
在瑞典,从肺结核患者(n = 7)、接触过结核病菌的人(n = 7)和对照组(n = 9)中收集口腔拭子。使用Illumina MethylationEPIC芯片确定DNAm状态。
我们在患者和对照组之间鉴定出5644个显著差异甲基化的CpG位点。对在肯尼亚和秘鲁收集的样本验证队列(患者,n = 26;接触过结核病菌的人,n = 9;对照组,n = 10)进行分析,证实了DNAm特征。我们鉴定出一个结核病共识疾病模块,该模块在与结核病相关的基因中显著富集。最后,我们使用机器学习鉴定出一组对结核病具有判别力的7个CpG位点,并开发了一种结核病分类器。在验证队列中,该分类器的曲线下面积为0.94,灵敏度为0.92,特异性为1。
总之,本研究结果显示了利用口腔拭子的DNAm特征探索结核病新诊断策略的临床意义。