Transcriptomic profiling facilitates classification of response to influenza challenge

Area of Science:

• Immunology
• Genomics
• Infectious Diseases

Background:

• Influenza vaccination coverage has increased, yet influenza-related mortality remains a concern.
• Current methods for classifying symptomatic versus asymptomatic influenza infection in challenge studies rely on artificial thresholds, limiting accuracy.
• Development of better influenza vaccines necessitates improved methods for assessing vaccine efficacy.

Purpose of the Study:

• To develop a more quantitative method for classifying symptomatic influenza infection in challenge studies.
• To identify host gene expression signatures associated with symptomatic influenza infection.
• To validate a predictive gene set for symptomatic influenza in an independent study.

Main Methods:

• Genome-wide gene expression profiling of peripheral blood from 22 healthy adults (11 vaccinated) inoculated with H3N2 influenza.
• Analysis of dynamic transcriptomic data combined with clinical parameters post-challenge (0, 12, 24, 48 h).
• Identification and validation of a six-gene signature (CCL2, SEPT4, LAMP3, RTP4, MT1G, OAS3) for classifying symptomatic infection.

Main Results:

• Significant variation in symptomatic scoring was observed among individuals with confirmed influenza.
• Four subjects with severe influenza showed differential gene expression in 1103 probes at 48 h post-challenge compared to others.
• A six-gene signature accurately classified symptomatic infection and replicated findings in an independent dataset.

Conclusions:

• Differential gene expression signatures are indicative of influenza challenge response.
• The expression of six specific genes can reliably classify an individual's response to influenza challenge.
• Genomic influenza response classification is reproducible in independent studies, offering potential for improved clinical trial assessment.