Schadron Tristan, van den Beld Maaike, Mughini-Gras Lapo, Franz Eelco
Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, Netherlands.
Institute for Risk Assessment Sciences, Utrecht University, Utrecht, Netherlands.
Front Microbiol. 2024 Sep 13;15:1460335. doi: 10.3389/fmicb.2024.1460335. eCollection 2024.
Improvements in sequencing quality, availability, speed and costs results in an increased presence of genomics in infectious disease applications. Nevertheless, there are still hurdles in regard to the optimal use of WGS for public health purposes. Here, we discuss the current state ("") and future directions ("") based on literature regarding the use of genomics in surveillance, hazard characterization and source attribution of foodborne pathogens. The future directions include the application of new techniques, such as machine learning and network approaches that may overcome the current shortcomings. These include the use of fixed genomic distances in cluster delineation, disentangling similarity or lack thereof in source attribution, and difficulties ascertaining function in hazard characterization. Although, the aforementioned methods can relatively easily be applied technically, an overarching challenge is the inference and biological/epidemiological interpretation of these large amounts of high-resolution data. Understanding the context in terms of bacterial isolate and host diversity allows to assess the level of representativeness in regard to sources and isolates in the dataset, which in turn defines the level of certainty associated with defining clusters, sources and risks. This also marks the importance of metadata (clinical, epidemiological, and biological) when using genomics for public health purposes.
测序质量、可及性、速度和成本的提高使得基因组学在传染病应用中的应用日益增多。然而，在将全基因组测序（WGS）用于公共卫生目的的最佳利用方面仍然存在障碍。在此，我们基于有关基因组学在食源性病原体监测、危害特征描述和来源追踪方面应用的文献，讨论当前状况（“”）和未来方向（“”）。未来方向包括应用新技术，如机器学习和网络方法，这些方法可能克服当前的不足。这些不足包括在聚类划分中使用固定的基因组距离、在来源追踪中辨别相似性或缺乏相似性，以及在危害特征描述中确定功能的困难。尽管上述方法在技术上相对容易应用，但一个总体挑战是对这些大量高分辨率数据的推断以及生物学/流行病学解释。从细菌分离株和宿主多样性的角度理解背景情况，有助于评估数据集中关于来源和分离株的代表性水平，这反过来又定义了与定义聚类、来源和风险相关的确定程度。这也凸显了在将基因组学用于公共卫生目的时元数据（临床、流行病学和生物学）的重要性。
