Cell Culture-Adaptive Mutations Promote Viral Protein-Protein Interactions and Morphogenesis of Infectious Hepatitis C Virus is a research paper published in Journal of Virology (2012). On theSindex it has a DataRank of 2.8. It has been cited 55 times, with 46 citing works in its 1-hop citation network.
ABSTRACTRecent genetic studies suggested that viral nonstructural (NS) proteins play important roles in morphogenesis of flaviviruses, particularly hepatitis C virus (HCV). Adaptive and compensatory mutations occurring in different NS proteins were demonstrated to promote HCV production in cell culture. However, the underlying molecular mechanism of NS proteins in HCV morphogenesis is poorly understood. We have isolated a cell culture-adapted HCV of genotype 2a (JFH1) which grew to an infectious titer 3 orders of magnitude higher than that of wild-type virus. Sequence analysis identified a total of 16 amino acid mutations in core (C), E1, NS2, NS3, NS5A, and NS5B, with the majority of mutations clustered in NS5A. Reverse genetic analysis of these mutations individually or in different combinations demonstrated that amino acid mutations in NS2 and NS5A markedly enhanced HCV production. Additionally, mutations in C, E1, NS3, and NS5B synergistically promoted HCV production in the background of NS2 and NS5A mutations. Adaptive mutations in NS5A domains I, II, and III independently enhanced HCV production, suggesting that all three domains of NS5A are important for HCV morphogenesis. More importantly, adaptive mutations greatly enhanced physical interactions among HCV structural and NS proteins, as determined by studies with coimmunoprecipitation and mammalian two-hybrid assays. Collectively, these findings demonstrate that adaptive mutations can enhance specific protein-protein interactions among viral structural and NS proteins and therefore promote the assembly of infectious HCV particles.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.604
From this paper's citation signal
Citation Network Contribution
2.2
From 45 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 22% comes from its base citations and 78% from the citation network (45 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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