TNO, Zeist, Netherlands.
Arla Foods Ingredients, Aarhus, Denmark.
Front Immunol. 2019 Feb 15;10:231. doi: 10.3389/fimmu.2019.00231. eCollection 2019.
Despite scientific advances it remains difficult to predict the risk and benefit balance of immune interventions. Since a few years, network models have been built based on comprehensive datasets at multiple molecular/cellular levels (genes, gene products, metabolic intermediates, macromolecules, cells) to illuminate functional and structural relationships. Here we used a systems biology approach to identify key immune pathways involved in immune health endpoints and rank crucial candidate biomarkers to predict adverse and beneficial effects of nutritional immune interventions. First, a literature search was performed to select the molecular and cellular dynamics involved in hypersensitivity, autoimmunity and resistance to infection and cancer. Thereafter, molecular interaction between molecules and immune health endpoints was defined by connecting their relations by using database information. MeSH terms related to the immune health endpoints were selected resulting in the following selection: hypersensitivity (D006967: 184 genes), autoimmunity (D001327: 564 genes), infection (parasitic, bacterial, fungal and viral: 357 genes), and cancer (D009369: 3173 genes). In addition, a sequence of key processes was determined using Gene Ontology which drives the development of immune health disturbances resulting in the following selection: hypersensitivity (164 processes), autoimmunity (203 processes), infection (187 processes), and cancer (309 processes). Finally, an evaluation of the genes for each of the immune health endpoints was performed, which indicated that many genes played a role in multiple immune health endpoints, but also unique genes were observed for each immune health endpoint. This approach helps to build a screening/prediction tool which indicates the interaction of chemicals or food substances with immune health endpoint-related genes and suggests candidate biomarkers to evaluate risks and benefits. Several anti-cancer drugs and omega 3 fatty acids were evaluated as test cases. To conclude, here we provide a systems biology approach to identify genes/molecules and their interaction with immune related disorders. Our examples illustrate that the prediction with our systems biology approach is promising and can be used to find both negatively and positively correlated interactions. This enables identification of candidate biomarkers to monitor safety and efficacy of therapeutic immune interventions.
尽管科学取得了进步，但仍难以预测免疫干预的风险和获益平衡。 几年来，人们已经基于多个分子/细胞水平（基因、基因产物、代谢中间产物、大分子、细胞）的综合数据集构建了网络模型，以阐明功能和结构关系。 在这里，我们使用系统生物学方法来确定涉及免疫健康终点的关键免疫途径，并对关键候选生物标志物进行排名，以预测营养免疫干预的不良和有益作用。 首先，进行文献检索以选择涉及过敏反应、自身免疫和抗感染和抗癌能力的分子和细胞动力学。 此后，通过使用数据库信息连接它们的关系，定义分子间的分子相互作用与免疫健康终点的关系。 选择与免疫健康终点相关的 MeSH 术语，导致以下选择：过敏反应（D006967：184 个基因）、自身免疫（D001327：564 个基因）、感染（寄生虫、细菌、真菌和病毒：357 个基因）和癌症（D009369：3173 个基因）。 此外，使用 Gene Ontology 确定了一系列关键过程，这些过程驱动了免疫健康障碍的发展，导致以下选择：过敏反应（164 个过程）、自身免疫（203 个过程）、感染（187 个过程）和癌症（309 个过程）。 最后，对每个免疫健康终点的基因进行了评估，结果表明许多基因在多个免疫健康终点中发挥作用，但也观察到每个免疫健康终点都有独特的基因。 这种方法有助于构建一种筛选/预测工具，该工具指示化学物质或食物物质与免疫健康终点相关基因的相互作用，并提出评估风险和获益的候选生物标志物。 评估了几种抗癌药物和欧米伽 3 脂肪酸作为测试案例。 总之，在这里我们提供了一种系统生物学方法来识别与免疫相关疾病相关的基因/分子及其相互作用。 我们的示例说明，使用我们的系统生物学方法进行预测是有前途的，可以用于发现负相关和正相关的相互作用。 这使得能够识别候选生物标志物来监测治疗性免疫干预的安全性和有效性。
