Netherlands Organisation for Applied Scientific Research (TNO), Zeist, Netherlands.
Arla Foods Ingredients, Aarhus, Denmark.
Front Immunol. 2020 Apr 17;11:644. doi: 10.3389/fimmu.2020.00644. eCollection 2020.
A healthy immune status is strongly conditioned during early life stages. Insights into the molecular drivers of early life immune development and function are prerequisite to identify strategies to enhance immune health. Even though several starting points for targeted immune modulation have been identified and are being developed into prophylactic or therapeutic approaches, there is no regulatory guidance on how to assess the risk and benefit balance of such interventions. Six early life immune causal networks, each compromising a different time period in early life (the 1st, 2nd, 3rd trimester of gestations, birth, newborn, and infant period), were generated. Thereto information was extracted and structured from early life literature using the automated text mining and machine learning tool: Integrated Network and Dynamical Reasoning Assembler (INDRA). The tool identified relevant entities (e.g., genes/proteins/metabolites/processes/diseases), extracted causal relationships among these entities, and assembled them into early life-immune causal networks. These causal early life immune networks were denoised using GeneMania, enriched with data from the gene-disease association database DisGeNET and Gene Ontology resource tools (GO/GO-SLIM), inferred missing relationships and added expert knowledge to generate information-dense early life immune networks. Analysis of the six early life immune networks by PageRank, not only confirmed the central role of the "commonly used immune markers" (e.g., chemokines, interleukins, , and other immune activation regulators (e.g., ), but also identified less obvious candidates (e.g., ). Comparison of the different early life periods resulted in the prediction of 11 key early life genes overlapping all early life periods (, and , and also genes that were only described in certain early life period(s). Concluding, here we describe a network-based approach that provides a science-based and systematical method to explore the functional development of the early life immune system through time. This systems approach aids the generation of a testing strategy for the safety and efficacy of early life immune modulation by predicting the key candidate markers during different phases of early life immune development.
健康的免疫状态在生命早期阶段受到强烈影响。深入了解生命早期免疫发育和功能的分子驱动因素是确定增强免疫健康策略的前提。尽管已经确定了几个靶向免疫调节的起点，并将其开发为预防或治疗方法，但对于如何评估此类干预措施的风险和收益平衡，尚无监管指导。生成了六个生命早期免疫因果网络，每个网络都包含生命早期的不同时间段（妊娠的第 1、2、3 个 trimester、出生、新生儿和婴儿期）。为此，使用自动化文本挖掘和机器学习工具：综合网络和动态推理组装器（INDRA）从生命早期文献中提取和构建信息。该工具识别相关实体（例如，基因/蛋白质/代谢物/过程/疾病），提取这些实体之间的因果关系，并将它们组装成生命早期免疫因果网络。使用 GeneMania 对这些因果生命早期免疫网络进行去噪，使用来自基因-疾病关联数据库 DisGeNET 和基因本体论资源工具（GO/GO-SLIM）的数据进行富集，推断缺失的关系，并添加专家知识以生成信息密集型生命早期免疫网络。通过 PageRank 对六个生命早期免疫网络进行分析，不仅证实了“常用免疫标志物”（例如趋化因子、白细胞介素等）的核心作用，还确定了不太明显的候选物（例如）。不同生命早期阶段的比较预测了 11 个关键的生命早期基因，这些基因重叠所有生命早期阶段（和，以及仅在某些生命早期阶段描述的基因（和）。总之，我们在这里描述了一种基于网络的方法，该方法提供了一种基于科学和系统的方法，通过随时间探索生命早期免疫系统的功能发育。这种系统方法有助于通过预测生命早期免疫发育不同阶段的关键候选标志物，为生命早期免疫调节的安全性和有效性生成测试策略。
