Oklahoma Animal Disease Diagnostic Laboratory, College of Veterinary Medicine, Oklahoma State University, Stillwater OK, USA.
Institute of Biosecurity and Microbial Forensics, Oklahoma State University, Stillwater OK, USA.
J Med Microbiol. 2023 Jun;72(6). doi: 10.1099/jmm.0.001720.
With expanding demand for diagnostics, newer methodologies are needed for faster, user-friendly and multiplexed pathogen detection. Metagenome-based diagnostics offer potential solutions to address these needs as sequencing technologies have become affordable. However, the diagnostic utility of sequencing technologies is currently limited since analysis of the large amounts of data generated, are either computationally expensive or carry lower sensitivity and specificity for pathogen detection. There is a need for novel, user friendly, and computationally inexpensive platforms for metagenome sequence analysis for diagnostic applications. In this study, we report the use of MiFi (Microbe Finder), a computationally inexpensive algorithm with a user-friendly online interface, for accurate, rapid and multiplexed pathogen detection from metagenome sequence data. Detection is accomplished based on identification of signature genomic sequence segments of the target pathogen in metagenome sequence data. In this study we used bovine respiratory disease (BRD) complex as a model. Using MiFi, multiple target bacteria and a DNA virus were successfully detected in a multiplex format from metagenome sequences acquired from bovine lung tissue. Overall, 51 clinical samples were assessed and MiFi showed 100 % analytical specificity and varying levels of analytical sensitivity (62.5 %-100 %) when compared with other traditional pathogen detection techniques, such as PCR. Consistent detection of bacteria was possible from lung samples artificially spiked with 10-10 c.f.u. of .
随着对诊断的需求不断扩大，需要更快、更用户友好和多重的病原体检测方法。基于宏基因组的诊断方法为满足这些需求提供了潜在的解决方案，因为测序技术已经变得负担得起。然而，测序技术的诊断效用目前受到限制，因为分析生成的大量数据在计算上既昂贵又降低了对病原体检测的敏感性和特异性。因此，需要新颖、用户友好且计算成本低廉的宏基因组序列分析平台，用于诊断应用。在这项研究中，我们报告了使用 MiFi（微生物发现者）的情况，MiFi 是一种计算成本低廉、具有用户友好在线界面的算法，用于从宏基因组序列数据中进行准确、快速和多重的病原体检测。检测是基于在宏基因组序列数据中识别目标病原体的特征基因组序列片段来完成的。在这项研究中，我们使用牛呼吸道疾病（BRD）复合体作为模型。使用 MiFi，我们能够以多重格式从牛肺组织中获得的宏基因组序列中成功检测到多种目标细菌和一种 DNA 病毒。总体而言，我们评估了 51 个临床样本，与其他传统病原体检测技术（如 PCR）相比，MiFi 显示出 100%的分析特异性和不同水平的分析灵敏度（62.5%-100%）。从人工用 10-10 c.f.u. 污染的肺样本中可以一致地检测到细菌。
