Data_Sheet_2_Holistic integration of omics data reveals the drivers that shape the ecology of microbial meat spoilage scenarios.PDF

BackgroundThe use of omics data for monitoring the microbial flow of fresh meat products along a production line and the development of spoilage prediction tools from these data is a promising but challenging task. In this context, we produced a large multivariate dataset (over 600 samples) obtained on the production lines of two similar types of fresh meat products (poultry and raw pork sausages). We describe a full analysis of this dataset in order to decipher how the spoilage microbial ecology of these two similar products may be shaped differently depending on production parameter characteristics.
MethodsOur strategy involved a holistic approach to integrate unsupervised and supervised statistical methods on multivariate data (OTU-based microbial diversity; metabolomic data of volatile organic compounds; sensory measurements; growth parameters), and a specific selection of potential uncontrolled (initial microbiota composition) or controlled (packaging type; lactate concentration) drivers.
ResultsOur results demonstrate that the initial microbiota, which is shown to be very different between poultry and pork sausages, has a major impact on the spoilage scenarios and on the effect that a downstream parameter such as packaging type has on the overall evolution of the microbial community. Depending on the process, we also show that specific actions on the pork meat (such as deboning and defatting) elicit specific food spoilers such as Dellaglioa algida, which becomes dominant during storage. Finally, ecological network reconstruction allowed us to map six different metabolic pathways involved in the production of volatile organic compounds involved in spoilage. We were able connect them to the different bacterial actors and to the influence of packaging type in an overall view. For instance, our results demonstrate a new role of Vibrionaceae in isopropanol production, and of Latilactobacillus fuchuensis and Lactococcus piscium in methanethiol/disylphide production. We also highlight a possible commensal behavior between Leuconostoc carnosum and Latilactobacillus curvatus around 2,3-butanediol metabolism.
ConclusionWe conclude that our holistic approach combined with large-scale multi-omic data was a powerful strategy to prioritize the role of production parameters, already known in the literature, that shape the evolution and/or the implementation of different meat spoilage scenarios.

背景
利用组学（omics）数据监测新鲜肉制品全生产线的微生物群落动态，并基于此类数据构建腐败预测工具，是一项兼具应用前景与挑战的研究课题。在此研究背景下，本研究针对两类相似的新鲜肉制品——禽肉香肠与生鲜猪肉香肠的生产线，构建了包含600余份样本的大型多变量数据集。本研究对该数据集开展全面分析，旨在阐明两类相似产品的腐败微生物生态学如何因生产参数特征的不同而呈现差异化的塑造模式。

方法
本研究采用整合分析策略，将无监督与有监督统计方法应用于多变量数据集，涵盖基于操作分类单元（OTU）的微生物多样性数据、挥发性有机物代谢组学数据、感官评价结果及生长参数；同时针对性筛选了潜在的非受控驱动因子[初始微生物群落组成]与受控驱动因子[包装类型、乳酸盐浓度]。

结果
本研究结果表明，禽肉香肠与猪肉香肠的初始微生物群落组成存在显著差异，且该差异对腐败发生模式以及包装类型等下游参数对微生物群落整体演替的影响均具有主导作用。研究还发现，针对猪肉的特定处理工序（如脱骨、脱脂）会筛选出特定的食品腐败菌，例如耐冷德格拉里奥菌（Dellaglioa algida），该菌在贮藏阶段会成为优势菌群。最后，通过生态网络重构分析，我们明确了与腐败相关的挥发性有机物生成所涉及的6条不同代谢途径，并从全局视角将这些代谢途径与不同的功能细菌类群以及包装类型的影响建立关联。例如，本研究揭示了弧菌科（Vibrionaceae）在异丙醇生成中的新功能，以及福知山侧乳杆菌（Latilactobacillus fuchuensis）与鱼肉乳球菌（Lactococcus piscium）在甲硫醇/二硫化物生成中的作用。此外，我们还发现肉明串珠菌（Leuconostoc carnosum）与卷曲侧乳杆菌（Latilactobacillus curvatus）在2,3-丁二醇代谢过程中可能存在互生关系。

结论
本研究表明，结合大规模多组学数据的整合分析策略，能够有效明确已有文献中提及的各类生产参数在塑造不同肉类腐败发生模式及其演替过程中的核心作用。