Seeking Windows of Opportunity to Shape Lifelong Immune Health: A Network-Based Strategy to Predict and Prioritize Markers of Early Life Immune Modulation
| Autor: | van Bilsen, Jolanda H M, Dulos, Remon, van Stee, Mariël F, Meima, Marie Y, Rouhani Rankouhi, Tanja, Neergaard Jacobsen, Lotte, Staudt Kvistgaard, Anne, Garthoff, Jossie A, Knippels, Léon M J, Knipping, Karen, Houben, Geert F, Verschuren, Lars, Meijerink, Marjolein, Krishnan, Shaji, Afd Pharmacology, Pharmacology |
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| Přispěvatelé: | Afd Pharmacology, Pharmacology |
| Rok vydání: | 2020 |
| Předmět: |
lcsh:Immunologic diseases. Allergy
0301 basic medicine Test strategy Immunology Psychological intervention text mining Computational biology Biology 03 medical and health sciences Child Development 0302 clinical medicine Immune system Acquired immunodeficiency syndrome (AIDS) Cytochrome P-450 CYP1A2 medicine Animals Humans early life Immunology and Allergy Gene Regulatory Networks Original Research Infant Newborn Computational Biology Infant biomarkers FOXP3 Forkhead Transcription Factors Immune modulation medicine.disease Early life Disease Models Animal machine learning 030104 developmental biology Immune System Diseases Immune System immune networks Chemokines lcsh:RC581-607 030215 immunology Immune activation |
| Zdroj: | Frontiers in Immunology, 11. Frontiers Media S.A. Frontiers in Immunology, Vol 11 (2020) Frontiers in Immunology |
| ISSN: | 1664-3224 |
| DOI: | 10.3389/fimmu.2020.00644 |
| Popis: | 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, IFN, TNF, TGFB, and other immune activation regulators (e.g., CD55, FOXP3, GATA3, CD79A, C4BPA), but also identified less obvious candidates (e.g., CYP1A2, FOXK2, NELFCD, RENBP). Comparison of the different early life periods resulted in the prediction of 11 key early life genes overlapping all early life periods (TNF, IL6, IL10, CD4, FOXP3, IL4, NELFCD, CD79A, IL5, RENBP, and IFNG), 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. |
| Databáze: | OpenAIRE |
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