The goal of this application is to identify predictive markers for outcomes of natural infection and/or vaccination with influenza, markers that are perhaps applicable to other infectious agents defined in the RFA. We have assembled a multi-disciplinary team composed of relevant infectious disease experts, clinical experts with access to appropriate patient samples, and experts in assay development, qualification, and validation. We will take advantage of a newly discovered predictive signature from the Khatri lab; unique clinical cohorts developed by the Dekker and Davis groups; Stanford?s Human Immune Monitoring Center (HIMC) for analysis of blood samples using Fluidigm, FACS and CyTOF; and expertise in multiplexed assay development by the Wang and Utz labs. Although beyond the scope of our application, the biomarker signature and the assays developed herein can be used to study other infectious agents and vaccine strategies that will be funded under this RFA mechanism. Our three specific aims will focus on the validation, application and translation of this signature.
Specific Aim 1 is to validate a panel of biomarkers for their ability to predict response to influenza vaccination and recovery from wild-type influenza virus infection.
Specific Aim 2 will characterize biological pathways identified in Aim 1 using well-established and previously described patient cohorts, and to compare the existing biomarker datasets that are available. Finally, Specific Aim 3 will develop a rapid, multiplexed assay for measuring transcripts using Giant MagnetoResistive (GMR) Sensors, with a long term goal to build a sensor capable of measuring multiple transcripts within minutes at point-of-assay. Our studies will enhance our understanding of the important biological processes involved in response to influenza and other pathogens, and will provide a clinically accessible immune metric for rapid or even real-time evaluation of patients and vaccines.
We will validate using PCR and microfluidics an 11 gene transcript profile, recently discovered using a ?Big Data? approach, that predicts influenza vaccine responsiveness. We will explore the underlying biology of the pathways we have discovered and will translate the assay to a sensitive and rapid new platform that uses Giant MagnetoResistive Sensors.
