Influenza immune escape under heterogeneous host immune histories

In a pattern called immune imprinting, individuals gain the strongest immune protection against the influenza strains encountered earliest in life. In many recent examples, differences in early infection history can explain birth year-associated differences in susceptibility (cohort effects). Suscep...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Trends in microbiology (Regular ed.) 2021-12, Vol.29 (12), p.1072-1082
Hauptverfasser: Oidtman, Rachel J., Arevalo, Philip, Bi, Qifang, McGough, Lauren, Russo, Christopher Joel, Vera Cruz, Diana, Costa Vieira, Marcos, Gostic, Katelyn M.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In a pattern called immune imprinting, individuals gain the strongest immune protection against the influenza strains encountered earliest in life. In many recent examples, differences in early infection history can explain birth year-associated differences in susceptibility (cohort effects). Susceptibility shapes strain fitness, but without a clear conceptual model linking host susceptibility to the identity and order of past infections general conclusions on the evolutionary and epidemic implications of cohort effects are not possible. Failure to differentiate between cohort effects caused by differences in the set, rather than the order (path), of past infections is a current source of confusion. We review and refine hypotheses for path-dependent cohort effects, which include imprinting. We highlight strategies to measure their underlying causes and emergent consequences. Influenza evolves to escape immunity, but antigenic substitutions rarely escape immunity equally in all hosts. Birth cohorts, each composed of hosts with similar infection histories, often differ in susceptibility to new influenza strains.Descriptive studies, which identify cohort-associated differences in susceptibility and sometimes relate them to differences in initial infection, are common. However, the strength and persistence of observed effects varies, and the mechanisms underlying these patterns are ill-defined.Understanding the epidemic and evolutionary impacts of cohort effects is not possible without a clear conceptual model for how these differences arise.We argue that focusing on specific, measurable causes and consequences of cohort effects is the best way to cut through semantic confusion, draw appropriate connections between observed epidemiological examples, and understand their evolutionary implications.
ISSN:0966-842X
1878-4380
1878-4380
DOI:10.1016/j.tim.2021.05.009