UMR 1014 SECALIM, INRA, Oniris, 44307 Nantes, France.
Nestec Ltd, Nestlé Research Konolfingen, Konolfingen, Switzerland.
Int J Food Microbiol. 2018 Dec 20;287:28-39. doi: 10.1016/j.ijfoodmicro.2018.04.015. Epub 2018 Apr 12.
According to the World Health Organization estimates in 2015, 600 million people fall ill every year from contaminated food and 420,000 die. Microbial risk assessment (MRA) was developed as a tool to reduce and prevent risks presented by pathogens and/or their toxins. MRA is organized in four steps to analyse information and assist in both designing appropriate control options and implementation of regulatory decisions and programs. Among the four steps, hazard characterisation is performed to establish the probability and severity of a disease outcome, which is determined as function of the dose of toxin and/or pathogen ingested. This dose-response relationship is subject to both variability and uncertainty. The purpose of this review/opinion article is to discuss how Next Generation Omics can impact hazard characterisation and, more precisely, how it can improve our understanding of variability and limit the uncertainty in the dose-response relation. The expansion of omics tools (e.g. genomics, transcriptomics, proteomics and metabolomics) allows for a better understanding of pathogenicity mechanisms and virulence levels of bacterial strains. Detection and identification of virulence genes, comparative genomics, analyses of mRNA and protein levels and the development of biomarkers can help in building a mechanistic dose-response model to predict disease severity. In this respect, systems biology can help to identify critical system characteristics that confer virulence and explain variability between strains. Despite challenges in the integration of omics into risk assessment, some omics methods have already been used by regulatory agencies for hazard identification. Standardized methods, reproducibility and datasets obtained from realistic conditions remain a challenge, and are needed to improve accuracy of hazard characterisation. When these improvements are realized, they will allow the health authorities and government policy makers to prioritize hazards more accurately and thus refine surveillance programs with the collaboration of all stakeholders of the food chain.
据世界卫生组织 2015 年估计,每年有 6 亿人因食用受污染的食物而生病,其中 42 万人死亡。微生物风险评估(MRA)是作为一种工具开发的,旨在减少和预防病原体及其毒素带来的风险。MRA 分为四个步骤来分析信息,并协助设计适当的控制选项以及实施监管决策和计划。在这四个步骤中,危害特征描述用于确定疾病结果的概率和严重程度,这是根据摄入的毒素和/或病原体剂量确定的。这种剂量-反应关系受到变异性和不确定性的影响。本文的目的是讨论下一代组学如何影响危害特征描述,更具体地说,如何改善我们对变异性的理解并限制剂量-反应关系中的不确定性。组学工具(如基因组学、转录组学、蛋白质组学和代谢组学)的扩展使我们能够更好地了解致病性机制和细菌菌株的毒力水平。毒力基因的检测和鉴定、比较基因组学、mRNA 和蛋白质水平的分析以及生物标志物的开发有助于构建机制剂量-反应模型来预测疾病严重程度。在这方面,系统生物学可以帮助识别赋予毒力的关键系统特征,并解释菌株之间的变异性。尽管在将组学纳入风险评估方面存在挑战,但一些组学方法已经被监管机构用于危害识别。标准化方法、重现性和从实际条件中获得的数据集仍然是一个挑战,需要提高危害特征描述的准确性。当实现这些改进时,它们将使卫生当局和政府决策者能够更准确地优先考虑危害,从而与食物链的所有利益相关者合作完善监测计划。