Vitense Philipp, Kasbohm Elisa, Klassen Anne, Gierschner Peter, Trefz Phillip, Weber Michael, Miekisch Wolfram, Schubert Jochen K, Möbius Petra, Reinhold Petra, Liebscher Volkmar, Köhler Heike
Institute of Mathematics and Computer Science, University of Greifswald, Greifswald, Germany.
Institute of Molecular Pathogenesis, Friedrich-Loeffler-Institut, Jena, Germany.
Front Vet Sci. 2021 Feb 3;8:620327. doi: 10.3389/fvets.2021.620327. eCollection 2021.
Analysis of volatile organic compounds (VOCs) is a novel approach to accelerate bacterial culture diagnostics of subsp. (MAP). In the present study, cultures of fecal and tissue samples from MAP-infected and non-suspect dairy cattle and goats were explored to elucidate the effects of sample matrix and of animal species on VOC emissions during bacterial cultivation and to identify early markers for bacterial growth. The samples were processed following standard laboratory procedures, culture tubes were incubated for different time periods. Headspace volume of the tubes was sampled by needle trap-micro-extraction, and analyzed by gas chromatography-mass spectrometry. Analysis of MAP-specific VOC emissions considered potential characteristic VOC patterns. To address variation of the patterns, a flexible and robust machine learning workflow was set up, based on random forest classifiers, and comprising three steps: variable selection, parameter optimization, and classification. Only a few substances originated either from a certain matrix or could be assigned to one animal species. These additional emissions were not considered informative by the variable selection procedure. Classification accuracy of MAP-positive and negative cultures of bovine feces was 0.98 and of caprine feces 0.88, respectively. Six compounds indicating MAP presence were selected in all four settings (cattle vs. goat, feces vs. tissue): 2-Methyl-1-propanol, 2-methyl-1-butanol, 3-methyl-1-butanol, heptanal, isoprene, and 2-heptanone. Classification accuracies for MAP growth-scores ranged from 0.82 for goat tissue to 0.89 for cattle feces. Misclassification occurred predominantly between related scores. Seventeen compounds indicating MAP growth were selected in all four settings, including the 6 compounds indicating MAP presence. The concentration levels of 2,3,5-trimethylfuran, 2-pentylfuran, 1-propanol, and 1-hexanol were indicative for MAP cultures before visible growth was apparent. Thus, very accurate classification of the VOC samples was achieved and the potential of VOC analysis to detect bacterial growth before colonies become visible was confirmed. These results indicate that diagnosis of paratuberculosis can be optimized by monitoring VOC emissions of bacterial cultures. Further validation studies are needed to increase the robustness of indicative VOC patterns for early MAP growth as a pre-requisite for the development of VOC-based diagnostic analysis systems.
挥发性有机化合物（VOCs）分析是一种加速副结核分枝杆菌亚种（MAP）细菌培养诊断的新方法。在本研究中，对感染MAP和未感染的奶牛和山羊的粪便及组织样本进行培养，以阐明样本基质和动物种类对细菌培养过程中VOC排放的影响，并确定细菌生长的早期标志物。样本按照标准实验室程序进行处理，培养管在不同时间段进行孵育。通过针阱微萃取对培养管的顶空气体进行采样，并采用气相色谱 - 质谱联用仪进行分析。对MAP特异性VOC排放的分析考虑了潜在的特征VOC模式。为了解决模式的变化问题，基于随机森林分类器建立了一个灵活且稳健的机器学习工作流程，该流程包括三个步骤：变量选择、参数优化和分类。只有少数物质源自特定基质或可归属于某一动物种类。变量选择程序认为这些额外的排放信息不足。牛粪便MAP阳性和阴性培养物的分类准确率分别为0.98，山羊粪便的分类准确率为0.88。在所有四种情况（牛与山羊、粪便与组织）下均筛选出六种表明存在MAP的化合物：2 - 甲基 - 1 - 丙醇、2 - 甲基 - 1 - 丁醇、3 - 甲基 - 1 - 丁醇、庚醛、异戊二烯和2 - 庚酮。MAP生长评分的分类准确率范围从山羊组织的0.82到牛粪便的0.89。错误分类主要发生在相关评分之间。在所有四种情况下均筛选出十七种表明MAP生长的化合物，其中包括六种表明存在MAP的化合物。在可见生长出现之前，2,3,5 - 三甲基呋喃、2 - 戊基呋喃、1 - 丙醇和1 - 己醇的浓度水平可指示MAP培养情况。因此，实现了对VOC样本的非常准确的分类，并证实了VOC分析在菌落可见之前检测细菌生长的潜力。这些结果表明，通过监测细菌培养物的VOC排放可优化副结核病的诊断。需要进一步的验证研究，以提高早期MAP生长指示性VOC模式的稳健性，这是基于VOC的诊断分析系统开发的先决条件。
