免疫功能与铁(Fe)稳态和同种异体密切相关。本生物信息学辅助综述的目的是双重的;(i)更新铁代谢及其与免疫系统的关系的当前知识,和(ii)对可能在应激诱导的免疫抑制期间充当潜在生物标志物的调节网络中心进行预测分析。利用一些文献和生物信息学数据库/存储库来回顾Fe代谢并补充优先蛋白质的分子描述。用于检索相互作用基因的搜索工具(STRING)用于构建蛋白质-蛋白质相互作用网络,以进行后续的网络拓扑分析。重要的是,Fe是一把敏感的双刃剑,其营养状况的两个极端可能对先天和适应性免疫产生有害影响。我们确定了明确连接的重要枢纽,属于两个集群:(i)肽抗原呈递免疫系统,参与Fe的氧化还原反应,血红素,和铁运输/运输;和(Ii)泛素化,内吞作用,以及与免疫细胞中Fe代谢相关的蛋白质的降解过程(例如,巨噬细胞)。确定的潜在生物标志物与当前的实验证据一致,包含在几个免疫学/生物标志物数据库中,和/或是针对不同压力条件的新兴遗传标记。尽管需要进一步验证,应该强调使用文献和生物信息学工具中的可用数据提取有意义的生物学应用的混合方法(人机协作)。
The immune function is closely related to iron (Fe) homeostasis and allostasis. The aim of this bioinformatics-assisted
review was twofold; (i) to update the current knowledge of Fe metabolism and its relationship to the immune system, and (ii) to perform a prediction analysis of regulatory network hubs that might serve as potential biomarkers during stress-induced immunosuppression. Several literature and bioinformatics databases/repositories were utilized to
review Fe metabolism and complement the molecular description of prioritized proteins. The Search Tool for the Retrieval of Interacting Genes (STRING) was used to build a protein-protein interactions network for subsequent network topology analysis. Importantly, Fe is a sensitive double-edged sword where two extremes of its nutritional status may have harmful effects on innate and adaptive immunity. We identified clearly connected important hubs that belong to two clusters: (i) presentation of peptide antigens to the immune system with the involvement of redox reactions of Fe, heme, and Fe trafficking/transport; and (ii) ubiquitination, endocytosis, and degradation processes of proteins related to Fe metabolism in immune cells (e.g., macrophages). The identified potential biomarkers were in agreement with the current experimental evidence, are included in several immunological/biomarkers databases, and/or are emerging genetic markers for different stressful conditions. Although further validation is warranted, this hybrid method (human-machine collaboration) to extract meaningful biological applications using available data in literature and bioinformatics tools should be highlighted.