RESUMEN
Age-related changes can significantly alter the state of adaptive immune system and often lead to attenuated response to novel pathogens and vaccination. In present study we employed 5'RACE UMI-based full length and nearly error-free immunoglobulin profiling to compare plasma cell antibody repertoires in young (19-26 years) and middle-age (45-58 years) individuals vaccinated with a live yellow fever vaccine, modeling a newly encountered pathogen. Our analysis has revealed age-related differences in the responding antibody repertoire ranging from distinct IGH CDR3 repertoire properties to differences in somatic hypermutation intensity and efficiency and antibody lineage tree structure. Overall, our findings suggest that younger individuals respond with a more diverse antibody repertoire and employ a more efficient somatic hypermutation process than elder individuals in response to a newly encountered pathogen.
Asunto(s)
Linfocitos B/inmunología , Linfocitos B/metabolismo , Inmunidad Activa , Receptores de Antígenos de Linfocitos B/metabolismo , Vacuna contra la Fiebre Amarilla/inmunología , Adulto , Animales , Anticuerpos Antivirales/inmunología , Femenino , Humanos , Inmunidad Activa/genética , Regiones Constantes de Inmunoglobulina/genética , Cadenas Pesadas de Inmunoglobulina/genética , Masculino , Persona de Mediana Edad , Receptores de Antígenos de Linfocitos B/genética , Hipermutación Somática de Inmunoglobulina , Vacunación , Fiebre Amarilla/prevención & control , Adulto JovenRESUMEN
Unique molecular identifiers (UMIs) show outstanding performance in targeted high-throughput resequencing, being the most promising approach for the accurate identification of rare variants in complex DNA samples. This approach has application in multiple areas, including cancer diagnostics, thus demanding dedicated software and algorithms. Here we introduce MAGERI, a computational pipeline that efficiently handles all caveats of UMI-based analysis to obtain high-fidelity mutation profiles and call ultra-rare variants. Using an extensive set of benchmark datasets including gold-standard biological samples with known variant frequencies, cell-free DNA from tumor patient blood samples and publicly available UMI-encoded datasets we demonstrate that our method is both robust and efficient in calling rare variants. The versatility of our software is supported by accurate results obtained for both tumor DNA and viral RNA samples in datasets prepared using three different UMI-based protocols.
