High definition viral vaccine strain identity and stability testing using full-genome population data – The next generation of vaccine quality control. Issue 43 (26th October 2015)

• Record Type: Journal Article
• Authors: Höper, Dirk; Freuling, Conrad M.; Müller, Thomas; Hanke, Dennis; von Messling, Veronika; Duchow, Karin; Beer, Martin; Mettenleiter, Thomas C.
• Is Part Of: Vaccine. Volume 33:Issue 43(2015)
• Page Start: 5829
• Page End: 5837
• Publication Date: 2015-10-26
• Subjects: Deep sequencing -- Viral population analysis -- Live-attenuated vaccines -- Vaccine quality control -- Population distance; Vaccines -- Periodicals; 615.372
• DOI: 10.1016/j.vaccine.2015.08.091
• Languages: English
• ISSNs: 0264-410X

Abstract

Abstract: Background: Vaccines are the most effective prophylactic public health tools. With the help of vaccines, prevention of infectious disease spread and, in concert with other measures, even eradication has become possible. Until now, licensing and quality control require the determination of consensus genome sequences of replication competent infectious agents contained in vaccines. Recent improvements in sequencing technologies now enable the sequencing of complete genomes and the genetic analysis of populations with high reliability and resolution. The latter is particularly important for RNA viruses, which consist of fluctuating heterogeneous populations rather than genetically stable entities. This information now has to be integrated into the existing regulatory framework, challenging both licensing authorities and vaccine producers to develop new quality control criteria. Methods: Commercially available modified-live oral rabies vaccines and their precursor strains were deep-sequenced to assess strain identity and relations between strains based on population diversity. Strain relations were inferred based on the Manhattan distances calculated between the compositions of the viral populations of the strains. Results: We provide a novel approach to assess viral strain relations with high resolution and reliability by deep sequencing with subsequent analysis of the overall genetic diversity within the viral populations. A comparison of our novel approach of inferring strain relations based on population data with consensus sequence analysis clearly shows that consensus sequence analysis of diverse viral populations can be misleading. Therefore, for quality control of viral vaccines deep sequencing analysis is to be preferred over consensus sequence analysis. Conclusions: The presented methodology allows for routine integration of deep sequencing data in vaccine quality control and licensing for highly reliable assessment of strain identity and stability.