Here we propose to utilize Network Biology methods to identify transcriptomic signatures that correlate with protective responses to immunization and characterize the pathways, hubs, and key mediators associated with effective neonatal immunization. The immune system is complex and comprises 1,500 to 5,000 individual gene products, as well as epigenetic events, miRNAs, posttranslational modifications, etc. Furthermore it is well recognized that the immune system is integrated with multiple physiologic systems, and is influenced by genetics, age, nutritional status, gender, environment and underlying diseases or health. Studying individual genes, proteins, pathways and/or cell types to understand immune processes has provided us with an incomplete picture of the intricacies of the cellular processes that take place in both health and disease. Thus understanding how individuals respond to immune challenge, for example vaccination, requires a more holistic systems-level approach. We have developed a substantial collection of skill sets and tools to enable consideration of all gene expression events occurring in the blood of newborns/infants (and/or adults), and ways of bioinformatically handing these data to enable network-oriented insights while considering all factors (termed meta-data, and including demographics and differences in clinical assessments) that might act as confounding variables. In particular we will use Network Biology to understand the impact of vaccination on immune status and ultimately what factors determine the relative success of vaccination in individuals. Our Transcriptomics Service Core (SC1) will develop transcriptomic data using the next generation sequencing method of RNA-Seq. Downstream analyses will utilize our customized databases and analysis tools. InnateDB is our popular (>6 million hits) open-source database and systems biology analysis platform of all the genes, proteins, contains experimentally validated molecular interactions, and pathways in innate immune responses of humans and other species. In addition we will apply our newest tool, the NetworkAnalyst platform, which features statistical, visual and network-based approaches for meta-analysis and systems-level interpretation of transcriptomic, and proteomic data. NetworkAnalyst delivers extremely fast network layouts, hub analysis and visualization enabling unbiased examination of large transcriptomic datasets as protein-protein interaction networks. Mining of the information for subnetworks, hubs and pathways permits unique insights into data and value-added insights into differences due to experimental conditions and stimuli. Critically, we have already de-risked all procedures for this project including sample collection, transport from remote locations, RNA-Seq and downstream bioinformatic analysis and have already demonstrated our ability to develop new insights/hypotheses into the potential mechanisms driving vaccine-induced neonatal responses in our pilot studies. We anticipate that our Core will make a substantial contribution to the overall success of this project.
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| Lux, Markus; Brinkman, Ryan Remy; Chauve, Cedric et al. (2018) flowLearn: fast and precise identification and quality checking of cell populations in flow cytometry. Bioinformatics 34:2245-2253 |
| Borriello, Francesco; van Haren, Simon D; Levy, Ofer (2018) First International Precision Vaccines Conference: Multidisciplinary Approaches to Next-Generation Vaccines. mSphere 3: |
