QuickFit: A High-Throughput RT-qPCR-Based Assay to Quantify Viral Growth and Fitness In Vitro.


Journal

Viruses
ISSN: 1999-4915
Titre abrégé: Viruses
Pays: Switzerland
ID NLM: 101509722

Informations de publication

Date de publication:
19 Aug 2024
Historique:
received: 22 07 2024
revised: 14 08 2024
accepted: 15 08 2024
medline: 1 9 2024
pubmed: 31 8 2024
entrez: 29 8 2024
Statut: epublish

Résumé

Quantifying viral growth rates is key to understanding evolutionary dynamics and the potential for mutants to escape antiviral drugs. Defining evolutionary escape paths and their impact on viral fitness allows for the development of drugs that are resistant to escape. In the case of HIV, combination antiretroviral therapy can successfully prevent or treat infection, but it relies on strict adherence to prevent escape. Here, we present a method termed QuickFit that enables the quantification of viral fitness by employing large numbers of parallel viral cultures to measure growth rates accurately. QuickFit consistently recapitulated HIV growth measurements obtained by traditional approaches, but with significantly higher throughput and lower rates of error. This method represents a promising tool for rapid and consistent evaluation of viral fitness.

Identifiants

pubmed: 39205294
pii: v16081320
doi: 10.3390/v16081320
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : NIAID NIH HHS
ID : AI174875
Pays : United States
Organisme : NIAID NIH HHS
ID : AI174276
Pays : United States
Organisme : NIDA NIH HHS
ID : DP2DA040254
Pays : United States
Organisme : NIDA NIH HHS
ID : 1DP1DA060607
Pays : United States

Auteurs

Nicolas M S Galvez (NMS)

Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, Harvard University, Cambridge, MA 02139, USA.

Maegan L Sheehan (ML)

Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, Harvard University, Cambridge, MA 02139, USA.

Allen Z Lin (AZ)

Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, Harvard University, Cambridge, MA 02139, USA.

Yi Cao (Y)

Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, Harvard University, Cambridge, MA 02139, USA.

Evan C Lam (EC)

Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, Harvard University, Cambridge, MA 02139, USA.

Abigail M Jackson (AM)

Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, Harvard University, Cambridge, MA 02139, USA.

Alejandro B Balazs (AB)

Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, Harvard University, Cambridge, MA 02139, USA.

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Classifications MeSH