Department of Microbial Pathogenesis and Immunology, Texas A&M Health Science Center, Bryan, TX 77807.
Netrias, LLC, Cambridge, MA 02142.
Proc Natl Acad Sci U S A. 2022 Apr 5;119(14):e2112886119. doi: 10.1073/pnas.2112886119. Epub 2022 Apr 1.
Bacterial pathogen identification, which is critical for human health, has historically relied on culturing organisms from clinical specimens. More recently, the application of machine learning (ML) to whole-genome sequences (WGSs) has facilitated pathogen identification. However, relying solely on genetic information to identify emerging or new pathogens is fundamentally constrained, especially if novel virulence factors exist. In addition, even WGSs with ML pipelines are unable to discern phenotypes associated with cryptic genetic loci linked to virulence. Here, we set out to determine if ML using phenotypic hallmarks of pathogenesis could assess potential pathogenic threat without using any sequence-based analysis. This approach successfully classified potential pathogenetic threat associated with previously machine-observed and unobserved bacteria with 99% and 85% accuracy, respectively. This work establishes a phenotype-based pipeline for potential pathogenic threat assessment, which we term PathEngine, and offers strategies for the identification of bacterial pathogens.
细菌病原体鉴定对于人类健康至关重要，过去一直依赖于从临床标本中培养生物体。最近，机器学习 (ML) 在全基因组序列 (WGS) 中的应用促进了病原体鉴定。然而，如果存在新的毒力因子，仅依靠遗传信息来识别新出现或新的病原体在根本上受到限制。此外，即使使用 ML 管道的 WGS 也无法辨别与毒力相关的隐匿遗传基因座相关的表型。在这里，我们着手确定是否可以使用发病机制的表型特征来评估潜在的致病威胁，而无需进行任何基于序列的分析。该方法成功地以 99%和 85%的准确率分别对先前机器观察到和未观察到的细菌的潜在致病威胁进行了分类。这项工作建立了一个基于表型的潜在致病威胁评估管道，我们称之为 PathEngine，并为细菌病原体的鉴定提供了策略。
